To assess the development of and variation in lengths of stay in Dutch hospitals and to determine the potential reduction in hospital days if all Dutch hospitals would have an average length of stay equal to that of benchmark hospitals.th percentile length of stay hospital).The potential reduction was calculated using data obtained from 69 hospitals that participated in the National Medical Registration (LMR). For each hospital, the average length of stay was adjusted for differences in type of admission  and case mix . We calculated the number of hospital days that theoretically could be saved by (i) counting unnecessary clinical admissions as day cases whenever possible, and (ii) treating all remaining clinical patients with a length of stay equal to the benchmark . The average length of stay in Dutch hospitals if all hospitals were able to treat their patients as the 15th percentile hospital would be 6 days and the number of day cases would increase by 13%.The average (mean) length of stay in Dutch hospitals decreased from 14 days in 1980 to 7 days in 2006. In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15th percentile hospital in the year 2000. In 2006 the mean length of stay ranged from 5.1 to 8.7 days. If the average length of stay of the 15Hospitals in the Netherlands vary substantially in case mix adjusted length of stay. Benchmarking – using the method presented – shows the potential for efficiency improvement which can be realized by decreasing inputs (e.g. available beds for inpatient care). Future research should focus on the effect of length of stay reduction programs on outputs such as quality of care. The aveThese findings may be explainable because until 2005, the financing system in the Netherlands did not encourage length of stay reduction. Hospitals were paid through a system based, in part, on hospital patient days. Medical specialists were paid separately from this system, mostly on the basis of a lump sum. Hospitals still had several reasons to reduce length of stay. For example, the Dutch Ministry of Health Care encouraged hospitals to reduce the number of beds from 3.8 to 2.0 beds per 1000 inhabitants. Hospitals feared that their new building plans would only be accepted if they anticipated this objective to reach 2.0 beds per 1000 inhabitants. Other rRecently, the introduction of a new financing system for hospitals, the Diagnosis Treatment Combination system (in Dutch: DBC) substantially increased the incentive for Dutch hospitals to shorten lengths of stay. This is a Dutch variation of the Diagnosis Related Group system; hospitals are paid for every DBC. At the start of the DBC-system the prices of 10% of all DBC's were negotiable between hospitals and health insurance companies. This percentage is growing. The objective is that 65–70% of all hospital care will be negotiable in 2011. For medical specialists the financing system will also change. The lump sum will be abolished and some kind of competitive system will be introduced as an intermediate phase to entirely free prices. The essence of the new financing system is to reorganize health care on a free market-basis. This new financing system gives hospitals and specialists a strong motivation to reduce costs and lengths of stay.These developments raise the question, how many hospital days potentially could be reduced in the Netherlands in the near future? Brownell et al. 1995) determined the potential savings by reducing length of stay in eight major acute care hospitals in Manitoba  determin. Both foth percentile hospital).In this study we present a method to make a realistic calculation of the potential reduction of hospital days. We will assess the development of lengths of stay in Dutch hospitals and calculate the potential reduction of length of stay if all hospitals would work as efficiently as the benchmark . All data were provided by research Institute Prismant. In the LMR, data are available of admissions in general and academic hospitals in the Netherlands. This information includes medical data such as diagnoses and surgical procedures as well as patient specific data, including age, gender and hospital stay. The LMR is not based on DBC's but diagnoses are classified by the ICD-9 and procedures by the Dutch Classification System of Procedures. There have been no major changes to these classification systems between 1991 and 2006.Participation in the LMR is voluntary. Until 2004, the participation percentage of hospitals to the LMR was nearly 100%. Since 2005 some hospitals  stopped their participation to the LMR because of the introduction of a second hospital registration: the registration of DBC's. This registration is obligatory and these hospitals gave priority to the DBC-registration instead of prejudicing the LMR-registration. Despite this diminishing number of participating hospitals we decided to use the 2006 data, the most recent available.In 2006, the total number of general and academic hospitals in the Netherlands was 96; 11 of these hospitals did not participate in the LMR and 16 hospitals participated but did not register their procedures in the LMR. We excluded both of these groups in our analysis. Sixty nine hospitals  did contribute to this study. The excluded hospitals did not have a specific pattern in their lengths of stay. In 2004 their combined average length of stay was the same as the combined average length of stay of the 69 hospitals that were included in our study. For this reason we assumed that the data used in this study were representative of all Dutch hospitals.A specialty was included if it had 100 or more clinical discharges. For eleven specialties, a number of hospitals were excluded because they produced too few discharges. The number of hospitals that were excluded varied from 57 hospitals for ophthalmology  to 1 hospital for orthopaedic surgery.In order to compare length of stay between hospitals we applied two adjustments:Dutch hospitals differ in their admission policies. In principle, there is a choice between outpatient-care, day-care and clinical admission. Outpatients are treated in outpatient departments, where they consult a doctor, nurse or paramedic. Day-care is defined as care given in a specific centre for day-care to patients that only stay for several hours during the day (no overnight). Clinical patients are treated in the clinical department. They occupy a bed on a clinical ward and they intend to stay one or more overnight(s). Some hospitals tend to treat patients presenting for small procedures in day-care, while other hospitals have a larger threshold to treat in day-care. They tend to treat these patients on a clinical ward. If these patients are admitted in a clinical department, their (relatively short) length of stay contributes to the overall mean length of stay, while it does not if these patients are treated in day-care. Thus, hospitals with a larger threshold to treat patients in day-care more easily reach a short mean length of stay. In order to correct for this we excluded all hospital days of patients admitted on a clinical ward while they in principle could have been treated in day-care. In our study the hospital stay of these patients was analyzed separately. This is in accordance with the recommendation Hanning  made to Admissions that could in principle have been treated in day-care were selected on the basis of the occurrence of the main procedure in day-care. We listed all day-care procedures that were performed at least 50 times in the Netherlands in 1997 in at least 5 hospitals. Clinical admissions with a main procedure that appeared on this list were counted as admissions that could in principle have been treated in day-care if they also complied with all of the following conditions:• Non-acute admission;• Admission not for delivery;• Patient did not die in hospital;• Maximum clinical length of stay of three days;• Only one specialty was responsible during the stay ;• No transfer to another hospital.The year 1997 was used as reference to ensure that admissions really could be treated in day-care and to avoid discussions between professionals. Therefore, there is a chance for underestimation.A valid comparison of lengths of stay requires case-mix adjustment. Therefore we computed for each hospital specialty a ratio of actual length of stay to expected length of stay. The expected length of stay was computed by Prismant. For each specialty the expected length of stay was based on the characteristics of its patients and the national mean length of stay that is associated with these characteristics. A ratio• Age, divided in 5 classes: 0, 1–14, 15–44, 45–64, 65+ years;• primary diagnosis. This is the main diagnosis that led to the admission); it includes about 1,000 diagnoses classified by the ICD9 in three digits;• procedures, classified by the Dutch Classification System of Procedures. The procedures considered depend on the diagnosis of the patient. On average it includes five procedure groups.Together these three parameters produced about 5 × 5 × 1,000 = 25,000 cells for which the mean length of stay is taken as the expected length of stay. An exception was made for patients with a length of stay of 100 hospital days and longer and for patients who died in hospital. For the latter two groups the expected length of stay was kept equal to the actual length of stay and consequently the ratio of actual length of stay to expected length of stay always was 1.th percentile hospital of each specialty was determined by ranking the quotients of actual to expected length of stay of all hospitals with 100 or more discharges for each specialty. The hospital with the lowest ratio of actual to expected length of stay was identified as the hospital with the shortest length of stay. For each specialty the length of stay at the 15th percentile hospital in this ranking was used as the standard for calculating the potential reduction of length of stay in all hospitals with a longer length of stay. For 2006, we calculated how many hospital days Dutch hospitals could have reduced if they had all been at least as efficient with their beds as the 15th percentile hospital.In an Australian benchmark Hanning used the minimum length of stay as the standard (at state level) . Brownelth percentile and not the minimum we captured potential rest variation which was not adjusted for.Experiences gained in our consultancy practice have shown that setting a realistic goal motivates medical specialists to reduce the length of stay. In the first years of our consultancy practice we used the minimum as the standard, but medical specialists had many problems with this approach. They continued emphasizing potential 'rest'-variation which was not standardized for. The use of the minimum as a standard discouraged them to work on improving the health care process. They saw it as an unattainable goal. By using the 15th percentile hospitals, we distinguished between hospital days that could be gained by substitution from clinical to day-care and hospital days that could be gained by treating clinical patients with a shorter length of stay.To calculate the length of stay reduction that Dutch hospitals can achieve based on the results of the 15An example for internal medicine:• In the 69 hospitals of this study the total number of hospital days in clinic and day-care was 1,467,522;• 215,587 patients were treated in day-care and 501 were treated in clinic only for 1 day;• 3,965 patients were admitted in clinic for a 2-day  or 3-day  stays but could potentially have been treated in day-care;• Treating them in day-care would save 2,867 + 1,098 + 1,098 = 5,063 hospital days, which is 0.3% of all hospital days in clinic and day-care combined;• Without the  day-care patients the total number of hospital days was 1,242,406, generated by 139,904 patients;• The 15th percentile hospital had a ratio of actual to expected length of stay of 0.95. Using this ratio to all expected lengths of stay of every hospital, the total gain in hospital days could be 162,868, which equalled 11.1% of all hospital days in clinic and day-care combined.As a result, for internal medicine the hospital days that could be gained by substitution from clinical to day-care was 0.3%. Hospital days that could be gained by treating clinical patients with a shorter length of stay amounted to 11.1%. The combined level was 11.4%.The length of stay in Dutch hospitals has been decreasing nearly every year since data have become available. In 1978 (which is the first year for which data from the LMR could be used) patients stayed in hospital for an average of 14.1 days, while in 2006 the average length of stay was reduced to only 6.6 days. This amounted to an average decrease of 0.3 days per year. In Figure th percentile hospital had an average length of stay of 7.4 days.In 2000, the shortest average length of stay was 5.7 days while the longest was 11.3 days. The 15th percentile hospital in the year 2000. Between 2000 and 2006 the 15th percentile decreased from 7.4 to 5.7 hospital days. The difference between the longest length of stay and the shortest length of stay also declined during this period: In 2000, the longest length of stay (11.3 days) was 2.0 times longer than the shortest length of stay (5.7 days), while in 2006 it was 1.7 times as long (longest 8.7 days and shortest 5.1 days).In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15Substantial variation in length of stay among hospitals will occur because not all hospitals have the same specialty (to the same extent) and also within a specialty hospitals can have a different patient mix.Figure In Table and reduction length of stay to 15th percentile hospital) to all hospital days in all Dutch hospitals.In the last column of Table th percentile hospital is relatively small, but because Internal Medicine is the largest specialty (in number of admissions), the absolute number of hospital days that can be saved is the highest of all specialties.Expressed in absolute numbers Internal Medicine is the specialty that has the largest number of hospital days to save, but expressed in percentages this potential reduction is the smallest. The standard deviation of the mean length of stay for Internal Medicine is relatively small when adjusted for case-mix (0.11). Therefore, the potential percentage reduction generated by reducing lengths of stay to the 15For General Surgery, the second largest specialty in the Netherlands, the data are similar. The standard deviation for General Surgery is the smallest of all specialties (0.09). The percentage of hospital days that could be saved is 11.6%. In comparison with Internal Medicine a larger portion of days could be gained by substitution to day-care.th percentile. The standard deviation is 0.40. This specialty mostly treats older multi-problem patients with multiple secondary diagnoses. They often are in need of long-term care in a nursing home or the community and may block hospital beds. They cannot leave the hospital in case of lacking nursing home capacity, insufficient home care arrangements or slow referral procedures. The differences in lengths of stay between hospitals that do not have problems in transferring these patients to long-term care facilities and hospitals that do have these problems are substantial.'Geriatrics and other specialties' has the largest percentage of hospital days that could be saved by reducing length of stay to the 15th percentile hospital – would be 6.0 days and day-care (that is not included in this length of stay) would grow by 13%.Overall the average length of stay in Dutch hospitals – if all hospitals would be able to treat their patients like the 15The continuous reduction of length of stay is all the more remarkable considering two main developments with an increasing effect on the average clinical length of stay:1. Since the eighties of the last century many hospitals have introduced day-care and have increasingly substituted (short-term) clinical admissions for day-care ,10.2. Another development which had an increasing effect on the average length of stay is the ageing of the patient population. In 1978, 19% of the admissions were 65 years or older. In 2006, this increased to 48%. On average, elderly people stay longer in hospitals than younger ones; in 2006 the 0–64-year-old patient stayed an average 5.2 days in hospital and the patients aged more than 64 years stayed an average of 9.1 days.In spite of these two developments the average length of stay decreased from year to year. We expect this to continue because in the coming years, the financing system in Dutch hospitals will more and more be based on market forces and the reimbursement through payments per diem will be abolished (as in the United States more than two decades ago ). The inThe potential reduction in length of stay may in fact be higher because of two methodological choices.First, we have chosen to use a 1997 list of treatments that could have been performed in day care. This list could have been longer if we had used more recent data as a reference. Currently, we are planning to update the list. Probably a new list will show more possibilities to substitute inpatient care into day-care. Until now, the health care system in the Netherlands gave only few incentives to treat patients in day-care. Updating the list at this moment will also give an underestimation of the possibilities for day-care. We think that, when the changes in the financing system have been carried out entirely, an update will clearly show more possibilities for day-care.Second, in our standardisation for patient mix, the expected length of stay was not used for patients with a length of stay of 100 hospital days and longer and for patients who died in hospital. For these two groups the realised length of stay was used instead of the expected length of stay. This means that the results are without the potential gain in efficiency for these two groups. However, it concerns a small number of patients. Only 0.1% of all patients had a length of stay of 100 hospital days and longer and 2.4% of all patients died in hospital.The variation in the quotients of actual length of stay and expected length of stay shows that for several specialties the mean score is not 1. This is the case especially for cardiothoracic surgery and for 'other specialties'. For these two specialties it is 'normal' that the quotient of actual and expected length of stay is higher than 1.0. For 'other specialties' it is known that many hospitals created a special ward for patients that could not be discharged in time to next care facilities like nursing homes. The length of stay of these patients was longer because of these waiting days and the hospitals booked for these patients an administrative transfer to 'other specialties'. The code 'other specialties' is also used for geriatrics. This specialty treats patients that may have the same age group, diagnosis- and procedure group as patients treated by other specialty, but often the patients treated by geriatrics have a more complex syndrome and stay longer in hospital because of their frailty. The variables for standardisation  do not seem to be sufficient for patients that are discharged by these two specialties. The variable 'specialty' should also been taken into account. Because we did our analysis for each separate specialty this was no problem for this study, but if length of stay is benchmarked on the level of hospitals, 'specialty' is a variable that should be taken into account.th percentile as benchmark and not the minimum. If a more sophisticated comparison data based on severity of illness were available, it would be possible to identify which subpopulations  were generating the largest numbers of excess days. This could be possible in the future because the Dutch hospital information system will be upgraded in 2010.For a large part of the data, adjustment for age, primary diagnosis and procedure amounts to an adjustment for severity of illness. However, we realise that there may still be residual case-mix related variation that is not adjusted for. We did not adjust for variations in comorbidities Neither did we account for variations between elective versus emergency cases. Both parameters were recorded in the LMR, but the completeness of the registration of these items varies between hospitals. We realise that the presence or absence of a large number of comorbidities and/or emergency cases at hospital level will affect overall length of stay of a particular hospital. However, this potential residual variation that is not adjusted for is one of the reasons why we used the 15Length of stay is often used as an indicator of efficiency ,11-13. EFrom a health system perspective, efficiency also depends on the efficiency of other sectors and on health outcomes . When leOn the contrary, we did not find research that showed that shorter lengths of stay in hospitals is related to adverse quality ,18,1,5. Brownell stated that 'reassuringly, shorter stays have not been found to be related to adverse patient outcomes. In fact, a study of almost 4000 US hospitals showed that hospitals that discharged patients more efficiently had lower post discharge death rates' . FinallyIf quality improvement leads to shorter lengths of stay and shorter lengths of stay can lead to a better quality of care, we are curious if hospitals with shorter length of stay have better outcomes than hospitals with a longer length of stay. In future work we will investigate the connection between length of stay and quality of care.th percentile hospital in 2006 is taken as the standard, a 14% reduction of all hospital days would be attained. This percentage varied substantially across medical specialties . Extrapolating the potential reduction of lengths of stay of the 69 hospitals (that participate in the LMR) to all 98 Dutch hospitals yields a total reduction of 1.8 million hospital days.The length of stay in Dutch hospitals has been decreasing for decades. Between 1978 and 2006 the average decrease was 0.3 days per year. In 2006 more than 80% of all hospitals reached an average length of stay lower than the 15th percentile hospital in the year 2000. In 2006 the length of stay ranged from 5.1 to 8.7 among the 69 hospitals. Still, a further reduction of lengths of stay is possible. If all hospitals had substituted their potential day-care patients to day-care and if the average length of stay of the 15The authors declare that they have no competing interests.IB designed the study, performed the analysis, interpreted the results, and drafted the manuscript. RH, TK and RJL helped to interpret the results and contributed to the discussion GPW supervised the study and participated in the formulation of the discussion. All authors reviewed and edited the manuscript for intellectual content.The pre-publication history for this paper can be accessed here:


Practicing mental repetition of “OM” has been shown to cause significant changes in the middle latency auditory-evoked potentials, which suggests that it facilitates the neural activity at the mesencephalic or diencephalic levels.dharana, and dhyana.The aim of the study was to study the brainstem auditory-evoked potentials (BAEP) in two meditation states based on consciousness, viz. ekagrata, i.e. single-topic lecture on meditation and (ii) cancalata, i.e. non-targeted thinking. The two meditation sessions were: (i) dharana, i.e. focusing on the symbol “OM” and (ii) dhyana, i.e. effortless single-thought state “OM”. All four sessions were recorded on four different days and consisted of three states, i.e. pre, during and post.Thirty subjects were selected, with ages ranging from 20 to 55 years  who had a minimum of 6 months experience in meditating “OM”. Each subject was assessed in four sessions, i.e. two meditation and two control sessions. The two control sessions were: (i) cancalata, ekagrata and dharana, but no change occurred during the dhyana session.The present results showed that the wave V peak latency significantly increased in cancalata, ekagrata and dharana, but there is no change during dhyana. It may be said that auditory information transmission was delayed at the inferior collicular level as the wave V corresponds to the tectum.These results suggested that information transmission along the auditory pathway is delayed during  The functions of the brain in meditation have been studied using different techniques. These include the electroencephalogram (EEG), evoked pEvoked potentials are used in meditation studies because a correlation between different evoked potential components and underlying neural generators is reasonably well worked out. Apart frStudies on short latency auditory-evoked potentials have not shown such clear changes. In that Patanjali’s yoga sutras (circa 900 BC).[dharana, which is characterized by focusing on the object of meditation and dhyana, which is a defocused state of mental expansiveness. With this background, the present study was undertaken to determine whether short latency auditory-evoked potentials would change in normal subjects in meditation considered as both dharana and dhyana sessions on separate days.More recently, we have attempted to understand meditation based on descriptions from an ancient yoga text. This is  900 BC). Based onguru).Thirty subjects were selected in the age range between 20 and 55 years  recruited from a residential setup, Swami Vivekananda Yoga Research Foundation, Bangalore, in south India. This age range was selected as short latency does not vary within this age range in healthy individuals. Only malTo assess the quality of the practice, visual analogue scale (VAS) was used at the end of each session.All of them expressed their willingness to participate in the experiment. The project was approved by the Institution’s Ethics Committee. The study protocol was explained to the subjects and their signed informed consent was obtained.Apart from their prior experience of “OM” meditation, they had undergone a 2-month orientation program in “OM” meditation under the guidance of an experienced meditation teacher.The condition to exclude subjects were any health disorder, especially psychiatric or neurological disorders, auditory deficits assessed by checking the auditory threshold of each ear separately and any medication that alters the functions of the nervous system. None of the subjects had to be excluded for these reasons.The order of the four sessions (i.e. two meditation sessions and two non-meditation control sessions) was randomized for each subject using a standard random number table. This wasekagrata, i.e. single-topic lecture on meditation and (ii) cancalata, i.e. non-targeted thinking. The two meditation sessions were: (i) dharana, i.e. focusing on the symbol “OM” and (ii) dhyana, i.e. effortless single-thought state “OM.” All four sessions consisted of three states, i.e. “pre” (5 min), “during” (20 min) and “post” (5 min).Each subject was assessed in four sessions, i.e. two meditation and two control sessions, to record BAEP. The two control sessions were: (i) The assessments were made on four different days, not necessarily on consecutive days, but at the same time of the day . The allocation of the subjects to the four sessions was randomized using a standard random number table. This wasµV, number of sweeps averaged 1,500, sweep width 10 ms, delay 0 ms. Binaural click stimuli, of alternating polarity, with 11.1 Hz frequency and 100 µS duration, were delivered through acoustically shielded earphones . The stimulus intensity was kept at 80 dB nHL. The rejection level was expressed as a percentage of the full-scale range of the analog-to-digital converter. This level was set at 90%. Silver chloride (Ag/AgCl) disc electrodes were placed on the scalp using a conductive water-soluble paste. The active electrode was at Cz according to the International 10–20 system[BAEP were recorded using the Nicolet Bravo system . The amplifier settings were as follows: low-frequency filter 100 Hz, high-frequency filter 3 KHz, sensitivity 50 20 system referencdharana and dhyana) as two separate states and two control states, i.e. cancalata or non-focused thinking and ekagrata or focusing without meditation and on more than one thought.Throughout all sessions, the subjects kept their eyes closed and followed pre-recorded instructions. The instructions emphasized carrying out the practice slowly, with awareness and relaxation. The meditators who participated in the study underwent 1 month of orientation sessions, where they practiced two phases that formed a continuum in meditation , and has to be taken through the stages of “streamlining the thoughts” (concentration or ekagrata) before moving on to the states of meditation. These are: one-pointed concentration or dharana and a defocused, effortless single-thought state or dhyana.These states are described in the traditional texts, i.e. the cancalata session, the 20-min period consisted of “non-targeted thinking,” during which the subjects were asked to allow their thoughts to wander freely as they listened to a compiled audio CD consisting of brief periods of conversation and talks on multiple topics recorded from a local radio station transmission. In the ekagrata session, the 20-min period consisted of focusing on a single topic, which was listening to a lecture on meditation, with multiple, yet associated, thoughts. In the dharana session, the 20-min period consisted of focusing on the symbol “OM.” During this session, they were asked to focus on the meaning of the syllable, OM, which is used as a symbol for the entire universe because OM is considered to represent “that which sustains everything.”[dhyana session, the 20 min of the practice consisted of meditation with effortless absorption in the single-thought state of the object of meditation, i.e. “OM.”In the rything.” In the dFor the two meditation sessions and the two control sessions, subjects were given guided instructions through separate recorded instructions for each session.For the BAEP, the peak latencies and peak amplitudes of all seven waves were calculated. Peak latency (msec) is defined as the time from stimulus onset to the point of maximum positive amplitude within the latency window. Peak amplitude (V) is defined as the voltage difference between a pre-stimulus baseline and the largest positive going peak within a given latency window.post hoc analyses with Bonferroni adjustment were performed to compare “pre” data with “during” and “post.”Statistical analysis was performed using SPSS (Version 10.0). The peak latencies and peak amplitudes of all seven waves were analyzed using repeated-measures analyses of variance (ANOVAs) and cancalata, ekagrata, dharana and dhyana, and Factor 2: States; with six levels, viz. pre, during (D1 to D4) and post. These repeated measures ANOVAs were carried out for the peak latency and peak amplitude of all levels.The repeated measures ANOVAs were performed with two “within–subject” factors, i.e. Factor 1: Sessions; with four levels, viz. post hoc analysis with Bonferroni adjustment for multiple comparisons between the mean values of different states .This was followed by a F=3.894, for df=2.678, 77.651, P< 0.015, Huynh-Feldt epsilon=0.893) and between States .The peak latency of wave V showed a significant difference between Sessions  separately showed a significant increase in the latency of wave V during the cancalata session , ekagrata session  and following the dharana session .F=6.515, for df=2.692, 78.060, P< 0.001, Huynh-Feldt epsilon=0.897) and between States .The amplitude of wave V also showed a significant difference between Sessions  separately showed no significant change in the peak amplitude of wave V (P>0.05). Also, there were no significant change in the other waves (P>0.05).Hence, the changes in wave V peak latency alone are presented in dharana and dhyana) and two control sessions . BAEP were recorded throughout all four sessions. There was a significant increase in the wave V peak latency during the cancalata, ekagrata and dharana sessions but there was no change during the dhyana session.In the present study, normal healthy volunteers who were experienced in practicing meditation on the syllable “OM” were assessed in two meditation . In contrast, an increase in wave V peak latency was found in the cancalata, ekagrata and dharana sessions. No such increase was obtained in the dhyana session. An increase in the latency of an evoked potential component is taken to suggest that sensory information processing at the level of the underlying neural generator is delayed.[cancalata, ekagrata and dharana mental states, sensory processing at the midbrain level was delayed. Another feature of the present study is that a difference is seen in the nature of the results in the two meditation sessions.In the literature, there is only one previous study of short latency auditory-evoked potentials in TM practitioners. In this study, at moderate stimulus intensities (40–50 dB), the wave V latency increased following meditation. In contr delayed. This sugdharana and dhyana states have been described in an ancient yoga text, namely Patanjali’s yoga sutras. In this text, dharana literary means “fixing the mind on a specific object” . The mind could be fixed on any point and. as long as disturbances from any corner are warded off, this mental state is called dharana. When dharana becomes effortless, it takes the form of dhyana, which is defined as the uninterrupted spontaneous flow of the mind toward the chosen object.In the introduction, it was already mentioned that cancalata and ekagrata are described in another ancient text, the Bhagavad Gita.[cancalata state is characterized by constant shifting of thoughts from one object to another. The ekagrata state is quite different from this and is similar to concentration. When haphazard thoughts are streamlined in a single direction, it is called ekagrata.In contrast to this, the two control sessions, i.e. vad Gita. The canccancalata), channelized thought in concentration (ekagrata) or in a state of channelized thought as in meditation (dharana), there was a delay in sensory information processing, as mentioned above at the mid-brain (possibly the inferior colliculus) level. In contrast, when the mental state was characterized by a lack of effort in dhyana, no such change occurred.Hence, irrespective of whether meditators were in a state of random thinking (dharana and dhyana states. The limitations of the present study are: (i) the fact that there was no attempt to vary stimulus intensities and hence the earlier findings of McEvoy, Frumkin and Harkins,[ekagrata, dharana and cancalata sessions were not different and cannot be ruled out as the VAS is essentially a subjective measure; no objective measure was taken. Only those subjects who achieved 75% of their ideal practice based of their subjective rating were included in the study. Again, the possibility that the sound stimulus influences all four practices cannot be ruled out. This is another limitation of the study.Further studies are required to understand whether neural relay centers further along the auditory pathway would also change differently in  Harkins, could nodharana and dhyana states of meditation based on BAEP.[17Despite these limitations, the present study does demonstrate a difference between the  on BAEP.1718


Antibiotics are widely-used medicines for which a more prudent use has been advocated to minimize development of resistance. There are considerable cross-national differences that can only partially be explained by epidemiological difference and variations in health care structure. The aim of this study was to explore whether cross-national differences in use of antibiotics (prescribed and non-prescribed) are associated with differences between national cultures as described in Hofstede's model of cultural dimensions .Country-level data of prescribed antibiotic use and self-medication with antibiotics were correlated to country-specific scores of cultural dimensions obtained from Hofstede. Data on use of antibiotics were provided by three European studies, based on different methods and/or countries: Self-medication with Antibiotics and Resistance in Europe (SAR), based on a survey in 2003 on reported use of antibiotics in 19 countries, the European Surveillance on Antimicrobial Consumption, based on distribution and reimbursement of antibiotics in ambulatory care (1997–2002), and the 2002 interview-based Eurobarometer study, asking whether respondents had taken antibiotics in the previous 12 months. These studies provided data on antibiotics use for 27 European countries in total, for which scores of cultural dimensions were also available. The SAR-study differentiated between prescribed antibiotics and self-medication with antibiotics.Significant positive correlations were found for Power Distance Index with use of prescribed antibiotics in the three studies (rho between 0.59 and 0.62) and with self-medication (rho = 0.54) in the SAR study. Positive significant correlations were found for the Uncertainty Avoidance Index with the use of antibiotics as reported in two studies . Masculinity was not significantly correlated, except in one study after controlling for GDP (r = 0.81). For Individualism and Long-Term Orientation no significant correlations were found.Power Distance is a cultural aspect associated with antibiotic use, suggesting that the culture-specific way people deal with authority is an important factor in explaining cross-national differences in antibiotic use. There are indications that Uncertainty Avoidance also plays a role but further research is needed to better understand the complex effect of cultural dimensions. Antibiotics are important and widely used medicines ,2. ThereMany studies have shown the relevance of characteristics of the doctor ,6 or theSeveral models and questionnaires have been developed to analyse cultural differences -21. One Power Distance refers to the degree of hierarchy in a country. It has been defined by Hofstede as the extent to which the less powerful members of organizations and institutions accept and expect that power is distributed unequally. A lower score indicates a preference for deliberation and mutual dependence between subordinates and their chief and authority based on secular/rational arguments. Individualism refers to the prevalence of the interests of an individual versus the group. This dimension is defined as the degree to which individuals are integrated into groups. A higher score indicates more emphasis on individualism. Masculinity, as opposed to femininity, refers to a culture in which the emotional roles of the two genders are clearly separated. A higher score indicates for instance that a culture is assertive and competitive. Uncertainty Avoidance deals with a society's tolerance for uncertainty and ambiguity. A higher score indicates that people feel uncomfortable in novel, unknown or surprising situations. Values associated with Long Term Orientation are thrift and perseverance; in contrast, a low score on this dimension implies values associated with Short Term Orientation: respect for tradition, fulfilling social obligations, protecting one's 'face' and expecting quick results.While the influence of cultural dimensions on illness behaviour and use of medicines has been suggested in previous studies, ,25 very Although antibiotics are used worldwide and their effectiveness is unquestionable, these medicines are the result of a particular view on illness, the so-called germ paradigm ,28. It iOne of the authors (RD) postulated more that a decade ago what effect on antibiotic use could be expected of these cultural dimensions ,30.Power Distance has to do with preferences of how persons with a different status communicate with each other. Hence, one might expect that this will also play a role in patient-doctor communication. A preference for deliberation about the use and sense of antibiotics might be favoured in countries with low Power Distance, as contrasted with a 'doctor knows best'-attitude in countries scoring high for Power Distance.Another hypothesis was that countries with high Uncertainty Avoidance would use more antibiotics because doctors try to avoid the ambiguity in cases of ailments such as 'a cold' or 'the flu', which are usually – but not always – trivial, self-limiting viral infections. Prescribing antibiotics is then a coping-strategy, much like a ritual, to reduce the uncertainty. In such countries patients too might have problems accepting a doctor saying no exact diagnosis can be given and therefore one should just 'wait and see'.With regard to Individualism physicians face the problem that prescribing antibiotics for the individual patient increases the risk of resistance for the population as a whole. The same holds with regard to Long versus Short term Orientations: antibiotics are expected to give quick results but the prescribing of antibiotics now raises the risk of making them ineffective in the future.Masculinity can be expected to correlate positively with the use of antibiotics, e.g. because these countries are strongly result-oriented and value a 'live to work' ethic. In such countries antibiotics might be favoured because they are often perceived and used as strong medicines that can get you back to work more quickly. Conversely, feminine cultures are more concerned with ecological problems and might therefore be expected to pay more attention to problems of antibiotic resistance.Based on these hypotheses, it has been suggested that Power Distance and Uncertainty Avoidance were correlated with antibiotic consumption ,17. HoweThe aim of this study is to test the aforesaid hypotheses and to further explore whether cultural dimensions are significantly correlated with cross-national differences in the prescribed use of antibiotics and self-medication with antibiotics.Data on the use of antibiotics were provided by three European studies, based on different methods and countries.In the 'Self-medication with antibiotics and Resistance in Europe' (SAR) study, data were obtained from a survey in 2003 on the reported use of systemic antimicrobial drugs in the last year in 19 European countries. A multistage sampling design was used in each participating country. Within each country, a region with average prescribed antibiotic consumption was chosen, based on available national data on the use of medicines. However, in some countries these data were not available at the start of the SAR study, and a region representative of the country's population in terms of age and sex was chosen . In each region an urban area  and a rural area  were selected. Persons over 18 years of age were randomly selected from population registries in the selected regions. The questionnaire was developed specifically for this survey in English, translated into national languages, and back-translated for consistency. The SAR study could differentiate between prescribed antibiotics and self-medication with antibiotics. The questionnaire is available in English from the corresponding author. More information is provided elsewhere . RespondIn the European Surveillance on Antimicrobial Consumption, data were based on either distribution or reimbursement of systemic antibiotics in ambulatory care for the period 1997–2002. Data were collected in 24 countries that provided internationally comparable data. Additional information about the characteristics of the data sources and data providers, obtained from the national ESAC representatives, was used to check validity of the data [The Eurobarometer provided data based on face-to-face interviews of stratified at random samples of inhabitants, aged 15 years or older, in 15 countries. Samples consisted of at least 1000 persons per country (except for Luxemburg (N = 602) and Northern Ireland (N = 302)) and were compared to the total population. Respondents were asked whether or not they had taken antibiotics in the last 12 months .Data on cultural dimensions were obtained from Hofstede . For an For the SAR study ethic or data committee approval was required and obtained for 11 countries . For theSpearman's correlation coefficient rho was used to calculate correlations between country-aggregated use of antibiotics and country-specific scores of cultural dimensions. A p-value of 0.05 or smaller was considered significant. Since some cultural dimensions are known to covary with affluence ,33, we eTwo countries from the SAR study and two from the ESAC study could not be included because of lack of data on Hofstede's cultural dimensions table . Hence cSignificant positive correlations were found for Power Distance Index with prescribed use of antibiotics in the three studies . For the SAR study correlations are not significant, however.For Individualism, Masculinity and Long-Term versus short-term Orientation the correlations were not significant.Self-medication was only significantly correlated with Power Distance (rho = 0.54).When controlling for the Gross Domestic Product (GDP) per capita, the correlations changed for Power Distance and Uncertainty Avoidance and became not significant. Correlations were also insignificant for Individualism and Long Term Orientation. For Masculinity the correlations tend to become stronger but they were significant for only one dataset (Eurobarometer: r = 0.81).The findings of positive correlations for antibiotic use with Power distance and, in 2 of the 3 studies with Uncertainty Avoidance, corroborate our hypotheses with regard to these dimensions. When controlling the correlations between cultural dimensions and antibiotics for wealth, only one correlation (for Masculinity and the Eurobarometer results for antibiotic use) is significant.Our findings may have implications with regard to the more rational use of antibiotics and might help us to improve our understanding of why there are such remarkable differences in antibiotic use. The effect of Power Distances is complex and might work on several levels. The preference for consultation and secular/rational arguments, which is typically of countries with low scores on Power Distance, may have implications for collaboration between doctors and other health care providers, such as pharmacists and nurses, as well as for doctor-patient communication. In countries with a high Power Distance, hierarchic differences are more manifested and doctors may therefore have a more pronounced (therapeutic) autonomy, whereas in other countries one can expect doctors to feel more part of a team. In the Netherlands (a country with a low Power Distance and one of the lowest use of antibiotics) Pharmacotherapy Counselling Groups, in which GPs and local community pharmacists regularly meet to exchange information about drug therapy, have been the spearhead of a successful policy for more rational use of antibiotics and other medicines ,35,36.Power Distance is also supposed to affect the doctor patient-relationship, as well as the relations between professionals. In countries with a high Power Distance it is likely that patients look up to their doctor, expecting great expertise and feeling little need for involvement in the decision-making. A doctor asking "what do you think yourself about taking an antibiotic?" would embarrass the 'modal' patient. Likewise, a physician acknowledging that he is not sure whether it is a viral or bacterial infection will not inspire much confidence in such a cultural context. Prescribing antibiotics has strong symbolic connotations and it can be seen as a sign of power and expertise . Hence aCountries with low Power Distance, in contrast, show a preference for shared decision-making in which the patient discloses his concerns and point of view, e.g. with regard to the pros and cons of antibiotics. It is true that patients often put pressure on the physician to prescribe antibiotics but doctors often overestimate patients demand . Taking If we apply the avoidance of uncertainty to medical situations, patients and physicians are expected to have an aversion to diagnostic uncertainty and will prefer a clear labelling of diseases. However, many symptoms, such as coughing, sore throat, mild fever, etc. are difficult to label and remarkable cross-cultural variations have been found ,39. BaseSelf-medication might be intuitively expected to be an expression of individualism. Talking F Rahme's saying into account "If I use an antibiotic too much, I am making it less useful for everyone"  we also The results with regard to Long-term orientation are not conclusive, probably because of the limited number of countries for which correlation could be calculated.We found few differences between the effects of cultural dimensions on prescribed use of antibiotics versus self-medication. This suggests that in countries with high incidence of self-medication with antibiotics, the prescription of antibiotics is also high  and thatThe finding that, after controlling for GDP/capita, the correlations become insignificant for Power Distance and Uncertainty Avoidance illustrates the complex relationship between a country's wealth and cultural dimensions. Also correlation values for Masculinity change. The original positive insignificant correlations become stronger (significantly for the Eurobarometer data) which is an indication of the complex relationship between wealth and cultural dimensions. Other studies have pointed out a possible effect of cultural dimensions on wealth and vice versa: Power Distances covaries negatively with a country's wealth. Uncertainty Avoidance influences consumer behaviour. Masculinity affects the way countries spent their money (e.g. masculine countries are inclined to prioritise economic growth over care for the environment and development aid). Long Term Orientation correlates with economic growth. ,33Our study has some limitations. First, due to the selection of regions in the SAR study, it is not certain that the chosen regions really reflect the national culture of these countries. Also the response rate for the SAR data was low in some countries. We therefore repeated the analysis, limited to countries with response rates of >40%. We also plotted the results and checked for the effect of possible outliers. These verifications corroborated our results. We also used data from others studies, based on other data sources and sampling methods to minimise the biases and restrictions of each particular study. Secondly, since we tested correlations for 5 cultural dimensions the chance of finding statistically significant correlations by chance increases in one dataset. We used different data sets to test hypotheses that were formulated before the data were available. Nevertheless extra care is warranted in interpreting the results. Thirdly, culture is certainly a much richer phenomenon than the reduction into 5 dimensions presented and Hofstede has been criticized for trying 'to measure the immeasurable'. ,45 HowevThe VSM questionnaire has been developed to compare countries. It is not meant to compare individuals but to describe relative differences between social systems only. A common pitfall is to look at culture as a kind of collective personality. Such an interpretation neglects the fact that cultures are the result of interaction between different – at the same time conflicting and complementary – personalities . TherefoWe are also aware of the fact that cultural dimensions might be the resultant of important factors (as we showed for the impact of GDP). However, the strength of cultural dimensions is that they are the resultant of many interacting factors and that they can put phenomena together that initially seem unconnected. An advantage is also that they enable quantitative analysis of cultural aspects in relation to other relevant issues such as the use of antibiotics. This is not possible using thicker descriptions of cultures, i.e. anthropological descriptions based on qualitative data. Therefore, we believe cultural dimensions are a simple but useful tool to measure the extremely complex concept of culture. Up till now, this approach has seldom been used to improve our understanding of the remarkable and partially unexplained national differences in antibiotic us, but has been successfully employed in dozens of studies, e.g. on management and communication -48. It pIn summary, our study indicates that Power Distance is a cultural aspect associated with antibiotics use, suggesting that the culture-specific way people deal with authority is an important factor in explaining cross-national differences in antibiotic use. Uncertainty Avoidance can lead to high consumer demand and defensive medicine, which may result in unnecessary use of antibiotics. The effect of cultural dimensions is complex because they influence a multitude of social phenomena on the macro and micro level, such as the wealth of a country. Further cross-national comparative research, e.g. based on larger samples and including also non-Western countries, is needed to better understand the correlations between cultural dimensions and the use of antibiotics and the reasons for this, so that our hypothetical explanations can be corroborated or falsified.The authors declare that they have no competing interests.RD participated in the design of the study and data collection, carried out the analysis and drafted the manuscript. LG participated in the design of the study and data collection, the analysis and helped to draft the manuscript. CSL participated in the design of the study, the data collection and critically revised the manuscript. GH participated in the design of the study and critically revised the manuscript. JC participated in the design of the study, helped with the analysis and critically revised the manuscript. GVDK participated in the design of the study and the data collection, and critically revised the manuscript. LD participated in the design of the study and the data collection, and critically revised the manuscript. FMH–R participated in the design of the study and coordinated it, helped with the analysis and the drafting of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Tinnitus is a frequent condition with high morbidity and impairment in quality of life. The pathophysiology is still incompletely understood. Electromagnetic fields are discussed to be involved in the multi-factorial pathogenesis of tinnitus, but data proofing this relationship are very limited. Potential health hazards of electromagnetic fields (EMF) have been under discussion for long. Especially, individuals claiming themselves to be electromagnetic hypersensitive suffer from a variety of unspecific symptoms, which they attribute to EMF-exposure. The aim of the study was to elucidate the relationship between EMF-exposure, electromagnetic hypersensitivity and tinnitus using a case-control design.Tinnitus occurrence and tinnitus severity were assessed by questionnaires in 89 electromagnetic hypersensitive patients and 107 controls matched for age-, gender, living surroundings and workplace. Using a logistic regression approach, potential risk factors for the development of tinnitus were evaluated.Tinnitus was significantly more frequent in the electromagnetic hypersensitive group (50.72% vs. 17.5%) whereas tinnitus duration and severity did not differ between groups. Electromagnetic hypersensitivity and tinnitus were independent risk factors for sleep disturbances. However, measures of individual EMF-exposure like e.g. cell phone use did not show any association with tinnitus.Our data indicate that tinnitus is associated with subjective electromagnetic hypersensitivity. An individual vulnerability probably due to an over activated cortical distress network seems to be responsible for, both, electromagnetic hypersensitivity and tinnitus. Hence, therapeutic efforts should focus on treatment strategies  aiming at normalizing this dysfunctional distress network. Tinnitus, the perception of sound in the absence of an external sound, is a frequent disorder of auditory perception, which is very difficult to treat Even if the pathophysiology of tinnitus remains incompletely understood, there is growing evidence that dysfunctional neuroplastic processes in the brain are involved. In particular, it is assumed that tinnitus might be the correlate of maladaptive neuroplastic changes due to distorted sensory input There has been an ongoing debate, whether tinnitus might be related to exposure to electromagnetic fields (EMF) Besides the hypothesized involvement in the generation of tinnitus, EMF-exposure has also been related to a variety of unspecific health symptoms . Despite a huge amount of studies investigating the health impact of EMF, no clear relationship between EMF-exposure and these unspecific health symptoms could be established and the majority of provocation studies failed to demonstrate such a relationship Due to the large sample size, the detailed clinical and neurobiological characterization and the control group, which was matched for age, gender and either living surroundings or workplace (as very rough proxies for EMF-exposure), this study population The study has been approved by the local ethics committee of the University of Regensburg. All participants gave written informed consent.Tinnitus occurrence and severity have been assessed in a sample of subjects who claimed themselves to be hypersensitive to electromagnetic fields (EMF-sensitive). Subjective electromagnetic hypersensitivity was defined by the occurrence of unspecific health complaints interfering with daily living and the subjective belief that these complaints are caused by named electromagnetic emission sources  Tinnitus occurrence and duration in the study population were assessed by the following questions: (1.) Do you currently perceive tinnitus? (2.) If yes, since how long? Furthermore, in all tinnitus sufferers tinnitus severity was assessed by a German translation in = 0.05, pout = 0.10) as model building strategy. The following variables were evaluated: (1) age at study entry; (2) subjective hypersensitivity to EMF, ability to differentiate electromagnetic evoked sensory stimuli, number of complaints in the Regensburg-EMF-complaint-list as an indicator of severity of subjective electromagnetic hypersensitivity; (3) gender; (4) measures of cortical excitability as determined by standard procedures with transcranial magnetic stimulation , anxiety disorder  and somatoform disorders  were significantly more frequent in the EMF-sensitive group compared to the control group . Furthermore, electromagnetic hypersensitive patients had a higher EMF-complaint level and a worse sleep quality . ElectroUsing a logistic regression analysis the following four items were found to be independent predictors for tinnitus . These wSince an association between tinnitus and sleep disorders is well known Interestingly, a reduced ability to discriminate real from sham magnetic pulses, which is typically diminished in subjectively electromagnetic hypersensitive subjects The primary aim of this study was to elucidate whether a potential relationship exists between EMF-exposure, electromagnetic hypersensitivity and tinnitus. The main finding is that tinnitus is much more frequent in subjectively electromagnetic hypersensitive patients compared to control subjects. Independent predictors of tinnitus occurrence were subjective belief of being electromagnetic hypersensitive, being male, a reduced sleep quality, and a reduced ability to discriminate real from sham electromagnetic evoked sensory stimuli. In contrast, no evidence for a relationship between EMF-exposure and tinnitus has been found.The observed prevalence of tinnitus of 17.5% in the control group is in accordance with findings from various epidemiological studies showing similar rates A new finding is the surprisingly high prevalence of tinnitus among the electromagnetic hypersensitive patients. Since the study design at least partially controlled for environmental EMF-exposure by recruiting patients and controls from the same private and working environments, the increased prevalence in the EMF-sensitive group can hardly be explained by differences in environmental EMF-exposure. Furthermore the utilization of mobile phones did not show any significant relationship to tinnitus. Our data thus indicate that the increased prevalence of tinnitus may rather be due to other factors raising the question about the nature of this relationship. One possibility is that tinnitus just represents another unspecific health symptom of subjectively electromagnetic hypersensitive patients. Another possibility is that tinnitus and subjective electromagnetic hypersensitivity share a common pathophysiology. A key feature repeatedly found in subjectively electromagnetic hypersensitive patients is reduced discrimination ability for magnetic pulses With the failure to prove a causal relationship between EMF-exposure and symptoms in subjectively electromagnetic hypersensitive patients The dysfunctional over-activation of this cortical neural network might be related to a disturbed representation of external and internal perceptions, which in turn could explain the reduced ability to discriminate real from sham electromagnetically evoked stimuli of electromagnetic hypersensitive patients Taken together these results point to a shared pathophysiology of subjective electromagnetic hypersensitivity and tinnitus. It may be hypothesized that these changes represent a key feature of somatoform disorders, which should be addressed in future studies.Although this study thus provides some interesting new findings, it is not without limitations. First, data were cross-sectional and correlation analyses were used, which makes it difficult to determine the exact nature of the relationships between the variables of interest. Hence, prospective, longitudinal studies are needed to establish the precise nature and the directions of the relationships explored in this study. Second, self-report measures were used for tinnitus assessment. Such measures may not accurately capture the phenomena under investigation. Third, the study design aimed at minimizing the influence of environmental physical  and social stressors. We are well aware that this design cannot guarantee equivalent EMF-exposure between both groups. However, it is noteworthy that the discrimination ability to differentiate real from sham magnetic stimuli, which was shown to be diminished in subjective electromagnetic hypersensitive patients and tinnitus sufferers, has been assessed under laboratory conditions in a double-blind, randomized design In conclusion, this study has shown that tinnitus is much more frequent among subjective electromagnetic hypersensitive patients whereas there is no hint for a relationship between tinnitus and exposure to electromagnetic fields. Rather, the correlation between tinnitus and electromagnetic hypersensitivity might be due to an individual vulnerability. Neurobiological characteristics of this increased vulnerability such as an oversensitive cortical distress network and an impaired discrimination ability for electromagnetically evoked sensory stimuli might be involved in the pathophysiology of both tinnitus and electromagnetic hypersensitivity and possibly also in other related perception disorders. Nevertheless, this hypothesis derived from our epidemiological study has to be confirmed in further studies by e.g. intervention studies aiming for a normalization of the postulated over-activated distress network in subjectively electromagnetic hypersensitive (e.g. cognitive behavioral therapy, which has been shown to be successful in electromagnetic hypersensitivity


In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization.We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies.Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research. Comparison and classification have been central pillars of biology since Linnaeus proposed his taxonomy and Darwin observed the mockingbirds on the Galapagos Islands. Like most scientific knowledge, biological laws and models are derived from comparing entities  and finding their similarities and differences. However, biology is unlike other sciences in that its knowledge can seldom be reduced to mathematical form. Thus, biologists either record their knowledge in natural language—for example, in scientific publications—or they must seek other forms of representation to organize it, such as classification schemes. When new entities arise, biologists approach them by comparing them to known entities and making inferences according to their degree of similarity.Comparing entities is not always trivial. For instance, while the sequences or structures of two gene products can be compared directly , the same is not true of their functional aspects. The difference is that sequences and structures have an objective representation and measurable properties, whereas functional aspects have neither. This does not mean that it is impossible to compare functional aspects, but that to be compared they must be expressed in a common and objective form.The advent of automated sequencing has had deep repercussions on knowledge representation in biology. As experimental methods shift in scope from the gene level to the genomic level, computational analysis is proving essential in handling the increasing amount of data. Thus it has become crucial to adopt common and objective knowledge representations to help knowledge sharing and computer reasoning. This need led to the development of ontologies for goals such as annotating gene products (Gene Ontology), annotating sequences (Sequence Ontology), and annotating experimental assays (Microarray and Gene Expression Data Ontology).The adoption of ontologies for annotation provides a means to compare entities on aspects that would otherwise not be comparable. For instance, if two gene products are annotated within the same schema, we can compare them by comparing the terms with which they are annotated. While this comparison is often done implicitly , it is possible to do an explicit comparison with semantic similarity measures. Within the context of this article, we define a semantic similarity measure as a function that, given two ontology terms or two sets of terms annotating two entities, returns a numerical value reflecting the closeness in meaning between them.The Gene Ontology (GO) molecular function, biological process, and cellular component. The nodes in the graph represent terms that describe components of gene product function. GO links the terms to each other by relationships, most commonly of the types ‘is a’ and ‘part of’, the former expressing a simple class–subclass relationship and the latter expressing a part–whole relationship. Gene products that are described by GO terms are said to be annotated with them, either directly or through inheritance, since annotation to a given term implies annotation to all of its ancestors (true path rule). The Gene Ontology Consortium is responsible for developing and maintaining GO terms, their relationships, and their annotations to genes and gene products of the collaborating databases. Moreover, GO Consortium is also responsible for developing tools that support the creation, maintenance, and use of all this information.GO provides a schema for representing gene product function in the cellular context. Several approaches are available to quantify semantic similarity between terms or annotated entities in an ontology represented as a DAG such as GO. This article distinguishes these approaches in the following way:Scope: Which entities they intend to compare, that is, GO terms versus gene products;Data source: Which components of the ontology they use, i.e., edges versus nodes;Metric: How they quantify and combine the information stored on those components.There are essentially two types of approaches for comparing terms in a graph-structured ontology such as GO: edge-based, which use the edges and their types as the data source; and node-based, in which the main data sources are the nodes and their properties. We summarize the different techniques employed in these approaches in distance, selects either the shortest path or the average of all paths, when more than one path exists. This technique yields a measure of the distance between two terms, which can be easily converted into a similarity measure. Alternatively, the common path technique calculates the similarity directly by the length of the path from the lowest common ancestor of the two terms to the root node Edge-based approaches are based mainly on counting the number of edges in the graph path between two terms While these approaches are intuitive, they are based on two assumptions that are seldom true in biological ontologies: (1) nodes and edges are uniformly distributed c can be quantified as the negative log likelihood,p(c) is the probability of occurrence of c in a specific corpus (such as the UniProt Knowledgebase), being normally estimated by its frequency of annotation. Alternatively, the IC can also be calculated from the number of children a term has in the GO structure Node-based approaches rely on comparing the properties of the terms involved, which can be related to the terms themselves, their ancestors, or their descendants. One concept commonly used in these approaches is information content (IC), which gives a measure how specific and informative a term is. The IC of a term The concept of IC can be applied to the common ancestors two terms have, to quantify the information they share and thus measure their semantic similarity. There are two main approaches for doing this: the most informative common ancestor (MICA technique), in which only the common ancestor with the highest IC is considered Approaches based on IC are less sensitive to the issues of variable semantic distance and variable node density than edge-based measures Other node-based approaches include looking at the number of shared annotations, that is, the number of gene products annotated with both terms molecular function terms, and gene products often participate in multiple biological processes and are located in various cellular components. Thus, to assess the functional similarity between gene products (within a particular GO category) it is necessary to compare sets of terms rather than single terms. Several strategies have been proposed for this, which we have divided into two categories: pairwise and groupwise approaches, as shown in Gene products can be annotated with several GO terms within each of the three GO categories. Gene product function is often described by several all pairs technique), while others consider only the best-matching pair for each term (best pairs technique). A global functional similarity score between the gene products is obtained by combining these pairwise semantic similarities, with the most common combination approaches being the average, the maximum, and the sum.Pairwise approaches measure functional similarity between two gene products by combining the semantic similarities between their terms. Each gene product is represented by its set of direct annotations, and semantic similarity is calculated between terms in one set and terms in the other (using one of the approaches described previously for comparing terms). Some approaches consider every pairwise combination of terms from the two sets . Functional similarity can be calculated either using graph matching techniques or, because these are computationally intensive, by considering the subgraphs as sets of terms and applying set similarity techniques.In vector approaches a gene product is represented in vector space, with each term corresponding to a dimension, and functional similarity is calculated using vector similarity measures. Vectors can be binary, with each dimension denoting presence or absence of the term in the set of annotations of a given gene product, or scalar, with each dimension representing a given property of the term .Since the first application of semantic similarity in biology, by Lord et al. The most common semantic similarity measures used with GO have been Resnik's, Lin's, and Jiang and Conrath's, which are node-based measures relying on IC While this measure is effective in determining the information shared by two terms, it does not consider how distant the terms are from their common ancestor. To take that distance into account, Lin's and Jiang and Conrath's measures relate the IC of the MICA to the IC of the terms being compared:However, being relative measures, To overcome this limitation, Schlicker et al. A constraint all of these measures share is that they look only at a single common ancestor (the MICA) despite the fact that GO terms can have several DCA. To avoid this, Couto et al. Bodenreider et al. Riensche et al. used coannotation data to map terms between different GO categories and calculate a weighting factor, which can then be applied to a standard node-based semantic similarity measure δ is the length in number of edges of the longest distance between term c1 and term c2. This measure was first applied to GO by Yu et al. Within the edge-based approaches, Pekar and Staab proposed a measure based on the length of the longest path between two terms' lowest common ancestor and the root (maximum common ancestor depth), and on the length of the longest path between each of the terms and that common ancestor Cheng et al. also proposed a maximum common ancestor depth measure, but weighted each edge to reflect depth molecular function terms is first derived from their co-occurrence in the same set of Interpro entries, and semantic similarity between two terms is calculated from the height of their lowest common ancestor in this “Functional Tree” rather than in the GO graph. With this approach, the authors intend to reveal natural biological links between the terms.A distinct approach was proposed by Pozo et al. c1 and its ancestor ac, the authors define the semantic contribution of ac to c1, as the product of all edge weights in the “best” path from ac to c1, where the “best” path is the one that maximizes the product. Semantic similarity between two terms is then calculated by summing the semantic contributions of all common ancestors to each of the terms and dividing by the total semantic contribution of each term's ancestors to that term. Othman et al. proposed a hybrid distance measure in which each edge is weighted by node depth, node link density, and difference in IC between the nodes linked by that edge Wang et al. Several measures for calculating the functional similarity between gene products have also been developed, as shown in molecular function and biological process aspects of GO The most common methods of measuring gene product functional similarity have been pairwise approaches based on node-based term measures, namely, Resnik's, Lin's, and Jiang and Conrath's. Lord et al. were the first to apply these measures, using the average of all pairwise similarities as the combination strategy Pairwise approaches have also been applied to edge-based measures: Wang et al. and Pozo et al. used a best-match average combination strategy with their measures A with terms t1 and t2 will be considered 100% similar to a gene product B with terms t1 and t3 under the maximum approach, which obviously does not reflect the differences between the gene products. As for the average approach, because it makes an all-against-all comparison of the terms of two gene products, it will produce counterintuitive results for gene products that have several distinct functional aspects. For instance, two gene products, A and B, that share the same two unrelated terms, t1 and t2, will be 50% similar under the average approach, because similarity will be calculated between both the matching and the opposite terms of the two gene products. The best-match average approach provides a good balance between the maximum and average approaches by considering all terms but only the most significant matching.Of the several combination strategies employed in pairwise measures, the best-match average variants are the best overall. The maximum approach can answer the question of whether two gene products share a functional aspect, but is unsuitable to assess their global similarity, as it is indifferent to the number of functional aspects they share and to the number of functional aspects in which they differ. For instance, a gene product Purely set-based approaches are not common, because few measures consider only direct annotations, but many graph-based approaches use set similarity techniques to simplify the problem of graph matching. The first graph-based measure to be applied to GO was that of Lee et al. Based also on the Jaccard index, Pesquita et al. have proposed the simGIC measure, in which each GO term is weighted by its IC Ye et al. proposed a normalized version of simLP that takes into account the minimum and maximum depths within each GO category Cho et al. developed a simpler groupwise approach, in which the semantic similarity between two gene products is given by the information content of the most informative term they share part_of relation, if its number of annotations is smaller than its child's, to comply with the logic that if the part exists, necessarily the whole does too.Other graph-based measures consider the probability of a gene product being annotated with a particular set of terms (annotation set probability). Lin et al. calculate the similarity between two gene product subgraphs as the frequency of occurrence of the graph resulting from the intersection of both subgraphs, that is, the frequency of gene products whose annotation subgraph contains the intersect graph As for vector-based approaches, Huang et al. developed a gene product similarity measure based on annotation profiles that includes GO terms as well as many other derived from varied sources for the tool DAVID Chabalier et al. also consider gene products as a vector of all GO terms, but weight them according to their IC. Semantic similarity is then calculated through the cosine similarity algorithm, which is commonly used to measure document similarity in information retrieval Given the variety of approaches and measures for semantic similarity, a fundamental question arises: How well does each measure capture the similarity in function between two gene products?Addressing this question is not trivial, because there is no direct way to ascertain the true functional similarity between two gene products. If there were, there would be no need to apply semantic similarity in the first place. However, there are independent properties, such as sequence similarity or coexpression data, that can be used as measures of similarity at different levels, and by correlating semantic similarity with such properties it is feasible to assess how well a given measure captures the similarity between gene products.The choice of how to evaluate measures is still a subject of debate, because no gold standard and few global comparative studies exist. Furthermore, most authors test only a few measures of interest for their particular applications. The first assessment study was done by Lord et al., who tested Resnik's, Lin's, and Jiang and Conrath's measures against sequence similarity using the average combination approach Later, Wang et al. tested the same measures against gene coexpression data, also using linear correlation. Although a high correlation was found, no conclusion was drawn about the relative performance of the measures Sevilla et al. also used gene coexpression data, but tested these measures with the maximum combination approach rather than the average. In agreement with Lord et al., they found Resnik's measure to perform best, and found the biological process aspect to have the highest correlation with gene coexpression. This result was far from unexpected, since genes with similar expression patterns are likely to be involved in the same biological processes Couto et al. tested the GraSM variation of these measures using a best-match average combination strategy, correlating sequence similarity with the number of Pfam families shared. They found the highest correlation with Jiang and Conrath's measure and the biological process aspect Schlicker et al. compared their measure with Resnik's measure by viewing the distribution of sequence similarity over semantic similarity, considering discrete levels. They found their measure to perform best, particularly in distinguishing orthologous gene products from gene products with other levels of sequence similarity, for both molecular function and biological process aspects Lei et al. tested several semantic similarity measures in an application for predicting subnuclear location, using the multi-class classification results (Matthews correlation coefficient) as an evaluation criterion Guo et al. evaluated simUI, simLP, Resnik's, Lin's, and Jiang and Conrath's measures on their ability to characterize human regulatory pathways. They concluded that the pairwise approaches tested performed better than groupwise approaches, with Resnik's measure being the best performing overall Wang et al. tested their measure against Resnik's by clustering gene pairs according to their semantic similarity and comparing the resulting clusters with a human perspective. They showed that their measure produced results closer to human expectations Recently, Pesquita et al. have done an evaluation of semantic similarity measures by modeling their behavior as function of sequence similarity. These authors compared Resnik's, Lin's, and Jiang and Conrath's measures, using several combination strategies, and also the groupwise measures simUI and simGIC, showing that the relationship between semantic similarity and sequence similarity is not linear for any of these measures. They found that the average and maximum combination strategies produce worse results than the best-match average and, consistent with Lord's and Sevilla's results, found that Resnik's measure performs better than the other two term-based measures. Overall the best measure for gene product similarity was shown to be simGIC, slightly surpassing Resnik's measure using the “pairwise-best pairs-average” approach in resolution Saccharomyces cerevisiae as the standard Xu et. al have also conducted an evaluation study using protein–protein interactions and gene expression datasets of Mistry et al. evaluated eleven measures: Resnik, Lin, and Jiang and Conrath with both the average and maximum approaches; three vector-based measures ; and TO and NTO. They investigated the correlation between measures and the correlation with sequence similarity. They found a good correlation between TO and Resnik's maximum and average. These three measures also correlated well to sequence similarity, with TO presenting the highest correlation.What we can draw from these studies is that there is no clear best measure for comparing terms or gene products. Different measures have performed differently under different circumstances, and a given measure can be well suited for a specific task but perform poorly in another. For instance, simUI was found by Guo et al. to be the weakest measure when evaluated for its ability to characterize human regulatory pathways, while Pesquita et al. found it to be fairly good when evaluated against sequence similarity. However, one result has been obtained consistently: pairwise measures using Resnik's term similarity outperform Lin's and Jiang & Conrath's methods in all studies except family similarity.There is also no clear best strategy for evaluating GO-based semantic similarity measures; there are arguments for and against most of the strategies employed. For instance, sequence similarity is well known to be related to functional similarity, but it is just as well known that there are gene products with similar sequences but distinct functions and vice-versa. Another example are Pfam families, which are related to global functional aspects of gene products, but will likely not be suitable to compare with detailed GO annotations.The rise in number of semantic similarity measures was accompanied by the development of tools to calculate them. Currently available tools fall into three categories: Web tools, standalone tools, or R packages see .http://xldb.fc.ul.pt/rebil/ssm) http://burgundy.cmmt.ubc.ca/GOToolBox) http://xldb.fc.ul.pt/biotools/proteinon) http://funsimmat.bioinf.mpi-inf.mpg.de) http://bioinformatics.clemson.edu/G-SESAME) Web tools, such as FuSSiMeG (http://gauss.dbb.georgetown.edu/liblab/dyngo.html) Standalone applications, such as DynGO (http://www.bioconductor.org/packages/2.2/bioc/html/SemSim.html) and GOvis (http://bioconductor.org/packages/2.3/bioc/html/GOstats.html) are part of the Bioconductor project, which comprises many R packages for bioinformatics and biostatistics applications. A more recent R based package for semantic similarity, csbl.go (http://csbi.ltdk.helsinki.fi/csbl.go/) The R packages SemSim . Most of the tools mentioned above also provide other types of services and information, such as protein interactions (ProteInOn), GO graph visualization , GO browsing (DynGO), and clustering .The application of semantic similarity allows gene products to be compared at different levels. This section presents several scenarios in which GO-based semantic similarity measures have been successfully applied.molecular function), the cellular and supracellular processes in which they are involved , and their cellular or extracellular location (cellular component). Comparing the molecular function aspect, we can measure the functional similarity between gene products and gain insight into function-related characteristics such as domains and active sites. The biological process aspect can be related to protein interaction, both physical and indirect (involved in the same process network), and thus can be used to predict them and to analyze coexpression data. The cellular component aspect can be linked to colocalization and in that context be used to validate physical interaction and localization-dependent functions and processes. Overall, GO-based semantic similarity measures have been applied mainly for validating and predicting functions and interactions, and for analyzing transcriptomics and proteomics data.For instance, GO-based semantic similarity can be used to compare gene products by their biochemical function , variable depth (terms at the same level have different levels of detail), and variable node density (some areas of the ontology have a greater density of terms than others). These are due to the irregular nature of biomedical knowledge and our limited understanding of it, and should be taken into account by semantic similarity measures, particularly edge-based measures, which are sensible to these issues. There are also problems with the use of annotated corpora in node-based measures, because these are biased by biomedical research interests (since terms related to subjects of research focus are expected to be more frequently annotated than other terms) and limited by current experimental techniques . Despite these issues, the use of IC on semantic similarity measures still makes sense probabilistically, if not always semantically, because commonly used terms are more likely to be shared by gene products than uncommonly used terms.IEA) indicates the annotation was inferred electronically  and not manually curated. Whether IEA annotations should be used or disregarded for semantic similarity is still a subject of open debate, because using them entails a loss of precision but not using them entails a loss of coverage  will have an impact in the IC values calculated for the terms, preventing their comparison. It is also important to stress that only results obtained with the same versions of GO's ontology and annotation data are comparable, since changes to both the ontologies and the annotations made with them affects semantic similarity .Another issue particular to GO is that not all annotations are equal, because evidence codes show the type of evidence that supports the annotation. While most evidence codes symbolize experimental methods and the corresponding annotations are manually curated, the most prevalent evidence code (ISS and IEA) to calculate the IC leads to data circularity, as there is direct dependency between both data sources. The same is true for the use of annotations inferred by physical interaction (IPI) when a measure is evaluated by correlation with protein-protein interactions, and other similar cases. To minimize the effect of data circularity, evaluation studies should  remove annotations based on evidence of the same nature as the data used to evaluate the measures.An important issue in the evaluation of semantic similarity measures based on the IC is that of data circularity between the data used to evaluate the measures and the GO annotations. For instance, if a given measure is evaluated by correlation with sequence similarity, then using annotations based on sequence similarity . These will give a measure of how similar two gene products are at their most similar aspect. For comparing multiple aspects, the best measures are “pairwise–best pairs–average” or groupwise approaches, since they allow for the comparison of several terms. However, depending on the level of detail desired, “pairwise–best pairs–average” or “groupwise–set” should be used for a higher degree of specificity (since only direct annotations are used) and “groupwise–vector” or graph for a more generalized similarity . To further minimize the relevance of specificity, unweighted graph or vector measures can be employed, so that high-level GO terms are not considered less relevant.However, having to analyze the dataset before deciding which measure to use can be cumbersome to researchers who just need a “quick and dirty” semantic similarity calculation. In this case, researchers should resort to one of the several semantic similarity tools available and use their good judgment in analyzing the results. Most semantic similarity measures proposed so far have shown fair if not good results, and for less detailed analyses any one of them can give a good overview of the similarities between gene products.Over the last decade, ontologies have become an increasingly important component of biomedical research studies, because they provide the formalism, objectivity, and common terminology necessary for researchers to describe their results in a way that can be easily shared and reused by both humans and computers. One benefit of the use of ontologies is that concepts, and entities annotated with those concepts, can be objectively compared through the use of semantic similarity measures. Although the use of semantic similarity in the biomedical field is recent, there is already a wide variety of measures available that can be classified according to the approach they use.There are two main semantic similarity approaches for comparing concepts: edge-based, which rely on the structure of the ontology; and node-based, which rely on the terms themselves, using information content to quantify their semantic meaning. Node-based measures are typically more reliable in the biomedical field because most edge-based measures assume that all relationships in an ontology are either equidistant or have a distance as function of the depth, neither of which is true for existing biomedical ontologies.Because biomedical entities are often annotated with several concepts, semantic similarity measures for comparing entities need to rely on sets of concepts rather than single concepts. There are two main approaches for this comparison: pairwise, in which entities are represented as lists of concepts that are then compared individually; and groupwise, in which the annotation graphs of each entity are compared as a whole.Several studies have been conducted to assess the performance of different similarity measures, by correlating semantic similarity with biological properties such as sequence similarity, or other classification schemas such as Pfam. Most measures were shown to perform well, but as few comprehensive studies have been conducted, it is difficult to draw any conclusion about which measure is best for any given goal.Until now, most research efforts in this area developed novel measures or adapted preexisting ones to biomedical ontologies, with most novel measures sporting an increased complexity compared to previous ones. This increased complexity is mainly a result of more recent measures combining several strategies to arrive at a final score. Although the need for improved measures is unquestionable, this trend fails to answer the most pressing community needs: (1) easy application to both small and large datasets, which would be best achieved by developing tools that are at once easy to use and powerful; and (2) elucidation of which measure is better fitted to the researcher's needs, which would imply comparative studies of all existing measures and approaches.Although important efforts in these two areas have already been made, semantic similarity is still far from reaching the status of other gene product similarities, such as sequence-based ones, in which fast and reliable algorithms coupled with the development of ever-growing databases has made them the cornerstone of present day molecular biology. One important step forward would be the development of a gold standard for gene product function that would allow the effective comparison of semantic similarity measures. Nevertheless, semantic similarity is not restricted to gene products and it can be expected that, as more biomedical ontologies are developed and used, these measures will soon be applied to different scenarios. It is then crucial that bioinformaticians focus on strategies to make semantic similarity a practical, useful, and meaningful approach to biological questions.