dataesr/scientific-paragraphs-categorization huggingface dataset:
Potential biases in the review process Only one review author conducted the initial screening of abstracts and applied the inclusion/exclusion criteria to produce a list of 608 potentially eligible studies. Two reviewers independently assessed these records for eligibility, extracted data and assessed the methodological quality. Two of the review authors (PMcC and IJH) were investigators in one of the included studies (Higginson 2009); although this represented potential for bias in the quality assessment (the study was considered of high quality), we used transparent criteria and the assessment was independently carried out by different review authors (BG/NC). The use of arbitrary cut points for considering studies and economic evaluations of high quality introduced subjectivity and it is possible that the use of more stringent cut points would lower the strength of the evidence on some outcomes.
This would have minimal impact on the results of our primary outcome but could affect the findings on symptom burden and caregiver grief, for example (where the evidence was graded strong based on narrative synthesis without meta-analysis). It should be noted that we did not pursue data retrieval from authors on secondary outcomes and on cost-effectiveness where the evidence was too heterogeneous to perform meta-analysis. We identified two Korean trials (Hwang 2009; Park 2011) which are pending classification as we wait for the authors' reply to decide eligibility. Furthermore, there are six ongoing studies eligible for inclusion (Abernethy 2006 in Australia, Allen 2012 in the US, Battaglia 2012 in Italy, Butler 2012 in the UK, Chvetzoff 2006 in France and Duursma 2011 in the Netherlands).
Notes Power considerations: "Power calculations were made based on the ability to detect a 10% change in pain score on a 0-10 scale. Study sample size was recalculated once preliminary data from the initial surveys was available to customise the power assessment. Based on the close similarity between intervention and control patients as well as the unexpectedly high severity of baseline symptoms, recruitment goals were amended from 150 to 50 in each group" Patients meeting inclusion criteria were identified through computerised searches of electronic medical records and recruited to intervention or control based on module affiliation. Primary physicians were given lists of patients they had seen in last 3 months and asked to identify the "sickest patients" with cancer, CHF or COPD who were expected to die within 1-5 years 4 USD10 grocery store incentive gifts provided to controls    a Results from repeated measures analysis of covariance (mixed-effects model applied to longitudinal data using random-subject effects to account for correlation between repeated outcome measurements on same individual).
a Authors used a random-effects model. Non-significant group main effects or group by time interaction terms indicated no group differences in pain scores or trajectories of change. a Authors used a random-effects model. Non-significant group main effects or group by time interaction terms indicated no group differences in pain scores or trajectories of change. a Authors used a random-effects model. Non-significant group main effects or group by time interaction terms indicated no group differences in scores or trajectories of change. a Authors used a random-effects model. Non-significant group main effects or group by time interaction terms indicated no group differences in scores or trajectories of change. Resource use data for each participant were collected retrospectively from the non-profit HMO mainframe database, from the time the patient enrolled in the study until the time of death or end of study (2002 to 2004).
Costs were calculated using actual costs for contracted medical services (services provided by non-HMO contracted facilities in Colorado) and proxy cost estimates for all services provided within the HMO. The authors explained that because services provided within the HMO were not billed separately, it was necessary to use proxy costs. Hospitalisation and ED cost estimates were calculated using aggregated data from more than 500,000 HMO patient records and included ancillary services such as laboratory and radiology. Costs of physician office visits included nurse and clerk expenses. Home health and palliative care visits were calculated using " average time spent on each visit" multiplied by the cost for each discipline's reimbursement rate. The authors MCOs provided resource use data on each participant for the duration of his/her enrolment in the study  (1999) (2000) (2001) , as well for the 6 months prior to enrolment (or all the time that the patient had been enrolled in the MCO prior to enrolment in the study).
The diary was designed for the study and was a complete record of the patient's use of health services for as long as 6 months following study entry  (1979) (1980) (1981) (1982). It was kept on a daily basis by the patient, if able, or otherwise by the caregiver (family member or friend). The diaries were monitored and collected biweekly by study interviewers. The diary recording of hospitalisations was validated, and corrected where necessary, by comparison with billing forms for all hospitalisations during the 6-month period and also cross-checked against a sample of Visiting Nurse Service records and records of the intervention team (the latter for the intervention group only). Resource use data were weighted to estimate costs " using current average charges for services in the community at the main provider agencies" where rates were constant (e.
g. visiting nurse service, health department, ED, nursing homes). The actual mean of hospital daily charges (USD300) was obtained from their billing forms and was used as the hospital per diem weight. The authors explained that since third-party reimbursements for physician house calls were well below actual costs, a more realistic physician home visit weighting factor was developed from estimates of total costs of a visit, including overhead and travel time, derived from a previous time-motion study of the intervention team. Costs were in USD (currency Tramarin 1992 Italy Limited description of data collection methods. The authors stated this was conducted prospectively using all available clinical documentation; costs were calculated with a specially designed software using a dBIII Plus database with more than 500 items.
At the same time their GP and district nurse were notified by separate letters that the patient was being included in the study and that they should record any contact with the patient in the folder. The time spent on home visits by different categories of staff for the care of these patients was continuously monitored on separate sheets. This included travelling time both ways and was registered in 15-minute units. In addition, medical records were examined to assess medical care utilisation. This was done prospectively for both groups. When a patient died, the data regarding their nursing requirements at home and in hospital were compiled. a Standard errors of estimates were based upon multiple regression analyses adjusted for sample differences. In calculating total per patient utilisation, the authors multiplied the utilisation per study day by the " average" number of study days (50) for hospice patients (intervention group).
Search strategies were refined with input from the Trials Search Coordinator of our Review Group (Cochrane PaPaS Review Group). Please see Appendix 1 for the MEDLINE search strategy in OVID and Appendix 2, Appendix 3, Appendix 4, Appendix 5, Appendix 6, Appendix 7 and Appendix 8 for all other search strategies. Results from searches in Cochrane databases, DARE, HTA and NHS EED were provided by the Trials Search Co-ordinator. The remaining searches and last update in November 2012 were conducted by BG/NC.
Six studies used a mix of methods but all included reports from patients or caregivers, or both. Three studies validated the information provided by participants. In Hughes 1992, prospective self reported use of VA services via patient diaries was confirmed using VA records, files and computer databases, while self reported use of private sector services was confirmed through letters or telephone calls to hospitals, doctors, clinics, nursing homes and home care agencies. Hughes stated that confirmation by a provider was obtained in 99% of cases and only confirmed data were included in the analyses. In Zimmer 1985, hospitalisations reported by participants in diaries were validated and corrected when necessary against billing records and a sample was crosschecked with the visiting nurse service and intervention records.
Use of healthcare services within and outside the VA was monitored by participants. To improve recall, patients were provided with a healthcare diary and were asked to record services for the 6month period of their participation in the study. They were contacted monthly by research staff to retrieve information and diaries were also examined by research staff in patients' homes during the 1 month and 6 month interviews. Self reported use of VA health care services was confirmed using VA records, files and computer databases. Self reported use of private sector health services was confirmed through letters or telephone calls to hospitals, doctors, clinic, nursing homes and home care agencies. Confirmation by a provider was obtained in 99% of cases and only confirmed utilisation data were included in the analysis.
Search strategies were refined with input from the Trials Search Coordinator of our Review Group (Cochrane PaPaS Review Group). Please see Appendix 1 for the MEDLINE search strategy in OVID and Appendix 2, Appendix 3, Appendix 4, Appendix 5, Appendix 6, Appendix 7 and Appendix 8 for all other search strategies. Results from searches in Cochrane databases, DARE, HTA and NHS EED were provided by the Trials Search Co-ordinator. The remaining searches and last update in November 2012 were conducted by BG/NC. 10. Database of Abstracts of Reviews of Effectiveness (DARE) (7 April 2010); 11. Health Technology Assessment (HTA) Database (7 April 2010); 12. NHS Economic Evaluation Database (NHS EED) (7 April 2010). 1. Cochrane Central Register of Controlled Trials (CENTRAL) (21 November 2012); 2. EMBASE (1980 to 21 November 2012); 3.
MEDLINE (1950 to 21 November 2012); 4. Cochrane Pain, Palliative and Supportive Care (PaPaS) Trials Register (11 May 2010); 5. Cochrane Effective Practice and Organisation of Care (EPOC) Trials Register (11 May 2010); 6. CINAHL (1981 to 13 April 2010); 7. EURONHEED (1980 to 13 April 2010); 8. PsycINFO (1806 to 13 April 2010); 9. Cochrane Database of Systematic Reviews (CDSR) (7 April 2010);.
Christakis NA, Clipp EC, McNeilly M, McIntyre L, Tulsky JA. Factors considered important at the end of life by patients, family, physicians, and other care providers. JAMA 2000;284(19):2476-82. McPherson 2003 Steinhauser 2000 McPherson CJ, Addington-Hall JM. Judging the quality of care at the end of life: can proxies provide reliable information?. Social Science & Medicine 2003;56(1): 95-109. Moore 2011 Grande 2000 Grande GE, Todd CJ. Why are trials in palliative care so Steinhauser KE, Tang 2002 Moore RA, Wiffen PJ, Derry S, McQuay HJ. Gabapentin difficult?. Palliative Medicine 2000;14(1):69-74. Tang ST, McCorkle R. Use of family proxies in quality of for chronic neuropathic pain and fibromyalgia in adults. Cochrane Database of Systematic Reviews 2011, Issue 3. [DOI: 10. 1002/14651858. CD007938.
Cochrane Database of Systematic Reviews 2011, Issue 7. [DOI: 10. 1002/14651858. CD009231] Teno 2013 Teno J, Gozalo PL, Leland NE, Miller SC, Morden NE, Scupp T, et al. Change in end-of-life care for Medicare beneficiaries: site of death, place of care, and health care transitions in 2000, 2005, and 2009. JAMA 2013;309(5): 470-7. Thomas 2004 Thomas C, Morris SM, Clark D. Place of death: preferences among cancer patients and their carers. Social Science and Medicine 2004;58(12):2431-44. Van Tulder 2003 Van Tulder M, Furlan A, Bombardier C, Bouter L. Updated method guidelines for systematic reviews in the Cochrane collaboration back review group. Spine 2003;28(12): 1290-9. Walsh 2009 Walsh D, Caraceni AT, Fainsinger R, Foley K, Glare P, Goh C, et al. Palliative Medicine. Philadelphia, PA: Saunders Elsevier, 2009.
After the first teleconsultation, the specialist nurse and a palliative care specialist advise the GP on the treatment policy for the patient. In addition to the weekly teleconsultations, the patient can also videophone the 24/7 support service of the homecare organization. The patient also has access to an information database, an internet-browser and some entertainment options via the telemedicine application Hwang 2009 (Continued) Park 2011 (Continued) Abernethy 2006 (Continued) Allen 2012 (Continued) Battaglia 2012 (Continued) Butler 2012 (Continued) Chvetzoff 2006 (Continued) Duursma 2011 (Continued) Control: to be confirmed by authors and pastoral care workers were also available according to patients' needs per day "on average") and illegal substance users as well as clients who have schizophrenia or are psychotic; Patient characteristics: pending Team: Rapid response teams (details below) Diseases: cancer (52) Deaths at end of study: pending Interventions Type: to be confirmed by authors Responsibility: not clear consumers who could not pass the Mental Status Questionnaire and those were already enrolled in hospice Deaths at end of study: pending Responsibility: pending Patient characteristics (interim analysis): median age 61 years, 56% male Withdrawals/other drop-outs: pending Service base: to be confirmed by authors Outcomes Satisfaction Description: intervention 1: GP Educational Outreach Visiting in addition to standard palliative care.
The remaining milk was aliquoted and stored at 220uC until tested by ELISA. Collected BTM samples were attributed to the postal code district of the respective dairy farm because the full addresses were not made available due to confidentiality issues. Samples that could not be unambiguously attributed to a postal code were excluded. Only a single BTM sample for each farm was used for further analysis. After sample preparation and data processing, a total of 19,910 BTM samples were serologically and statistically analysed. The number of samples per federal state ranged from 37 from Saarland to 8,841 from Lower Saxony. Detailed data are presented in Table  1 , whereas Figure  1  shows the distribution of sampled farms (number of farms per postal code district). Altogether, 2656 postal code districts were included in the study and between 1 and 174 farms per postal code district were sampled.
Analysis using Geographic Information System (GIS) Raw data were entered in Excel spreadsheets (MicrosoftH Office 2003) for descriptive analysis. Spatial risk factors that might be associated with the distribution of dictyocaulosis were tested for statistically significant associations with the ELISA results. GIS analysis was carried out with the following variables: (1) Dependent variables: (i) The ODR value for each sampled farm with ODR $0. 410 as positive (''10) and ,0. 410 as negative (''0''). (2) Independent variables: (ii) The postal code of each sampled dairy farm (map scale of the postal areas 1:250,000 Farm and cattle density, climate and land-use data were attributed to postal code areas as the common spatial unit and maps plotted using ArcGIS software (version 9. 3. 1; ESRI, Redlands, CA, USA) as described  [19].
Statistical Analysis All statistical analyses were performed using R (http://www. rproject. org/; including the libraries MASS, glmmML, lme4). A pvalue of #0. 05 was considered statistically significant. First, all variables listed in the section above were individually tested against the ELISA results by bivariate testing. Correlation between the variables was taken into account by building models that always contained temperature and altitude, and which included a landscape categories selected on the basis of biological criteria potentially related to the life cycle of D. viviparus. Variables either from the CLC or ATKIS dataset in the multivariate models.
Conclusions The results of the present survey demonstrate that bovine lungworm infections of adult dairy cattle are common in Germany. Indeed, dictyocaulosis in dairy herds is more frequent than expected from previous prevalence data obtained for the month of November in East Frisia, a German region with a high density of pastured dairy cattle  [10]. Statistically significant associations were obvious between seropositivity to D. viviparus in BTM samples and the proportion of grassed area and lentic water bodies and, so these variables can be considered as potential risk factors for dictyocaulosis. Increased attention should be paid to lungworm infections in adult cows and veterinarians should consider this parasitosis in the event of respiratory symptoms in dairy herds. Suspected lungworm infections can be substantiated by fecal examination or ELISA testing of clinically affected individuals.
Evaluating the composition of the human gut microbiota greatly facilitates studies on its role in human pathophysiology, and is heavily reliant on culture-independent molecular methods. A microarray designated the Human Gut Chip (HuGChip) was developed to analyze and compare human gut microbiota samples. The PhylArray software was used to design specific and sensitive probes. The DNA chip was composed of 4,441 probes (2,442 specific and 1,919 explorative probes) targeting 66 bacterial families. A mock community composed of 16S rRNA gene sequences from intestinal species was used to define the threshold criteria to be used to analyze complex samples. This was then experimentally verified with three human faecal samples and results were compared (i) with pyrosequencing of the V4 hypervariable region of the 16S rRNA gene, (ii) metagenomic data, and (iii) qPCR analysis of three phyla.
Fluorescence In Situ Hybridization (FISH) and fingerprinting techniques such as Denaturing Gradient Gel Electrophoresis (DGGE), Terminal Fragment Length Polymorphism (T-RFLP) are frequently used (reviewed in  [16] ). However, they generally lack resolution and do not allow high-throughput direct phylogenetic identification. More recently techniques such as DNA microarray hybridization and next-generation sequencing (NGS) have been developed granting further phylogenetic identification of microbiota diversity  [16, 17]. Microarray technology is a high throughput platform used to study numerous samples and to detect thousands of nucleic acids sequences simultaneously making it fast and user friendly. Phylogenetic DNA microarrays consist of several thousand probes, usually designed from rRNA gene sequence database targeting either specific organisms (e.
The authors suggested that the differences observed between the data from the two techniques might arise from a combination of the analysis of different hypervariable regions, the limited number of 16S rRNA gene sequences available for the probe design, and the ability of these probes to only target known 16S rRNA gene sequences. Phylogenetic microarray probe design can be performed using various software packages such as ARB  [24] , PRIMROSE  [25]  and ORMA  [26]  which have been widely used as they provide specific and sensitive probes to address sequences from databases. In spite of the exponential growth of data within international databases, our current understanding of microbial diversity is still incomplete. Microarrays coupled with explorative probe design strategies are, therefore, well suited to survey complete microbial communities, including microorganisms with uncharacterized sequences  [27].
The PhylArray  [28]  and the KASpOD  [29]  probe design software were developed to provide sensitive, specific and also explorative probes dedicated to phylogenetic microarrays  [28]. This innovative probe design strategy may help to overcome the main limitation of microarrays i. e. the inability to detect unknown sequences and thus, to survey uncharacterized microbial populations. In this study, we present the Human Gut Chip (abbreviated in HuGChip), a novel phylogenetic microarray. It is designed using the PhylArray software, and is intended to assess the human gut microbiota at the family level using 4,441 25-mer probes representing 66 families present in the human gut microbiota.
Microarray probe design and production The DNA microarray was designed using a custom 16S rRNA gene database. This was derived from the sequences described in 2007 by Rajilic-Stojanovicet al. [8]  and consisted of 1,052 sequences (longer than 1,000 nucleotides) which can be accessed at http://g2im. u-clermont1. fr/HuGChip/. The PhylArray software was used to design 25-mer probes  [28]. The first step of the PhylArray algorithm (Figure  1 ) is the extraction of all available sequences corresponding to the targeted family from our custom 16S rRNA curated database. Retrieved sequences are then aligned using the ClustalW program  [30]. A degenerate consensus sequences is then deduced from this multiple alignment, taking into account the sequence variability at each position. Degenerate candidate probes are then selected along the consensus sequence, and all non-degenerate combinations are checked for crosshybridizations against the 16S rRNA database.
The locus corresponding to each 25-mer degenerate probe is referred to hereafter as a ''region''. Among the combinations derived from each degenerate probe, some correspond to sequences that have not yet been deposited in the databases, namely explorative probes. Such probes should, therefore, allow the detection in this environment of undescribed microorganisms belonging to the targeted taxon. The best 5 ''regions'' of each consensus sequence, harbouring the best specificity for the taxon were selected to represent the taxon. Finally, these selected probes were subsequently verified by BLASTN  [31]  against the two other databases (Greengenes  [32] , SILVA  [33] ) containing microbial sequences from many different kinds of ecosystems. The microarray was synthesized by Agilent Technologies (Agilent Technologies, Palo Alto, CA) using the in situ surface attached synthesis  [34]  with a multiplex format of 8615k where each probe was randomly spotted in three replicates across the array to reduce biases caused by spatial variations.
Sample labelling and microarray hybridization, reading and analysis For each sample (faecal samples and the mock community), the non-fragmented purified 16S rRNA gene PCR products (1 mg) were labelled with either Cy3 or Cy5 using the Genomic DNA ULS labelling Kit (Agilent Technologies, Palo Alto, CA) following the manufacturer's instructions. For microarray hybridization, 100 ng of labelled artificial bacterial DNA mix and 250 ng of each labelled faecal sample were used (GEO accession number GSE44752). Hybridization was performed following the Agilent OligoaCGH hybridization protocol (Agilent Technologies, Palo Alto, CA) at 65uC for 24 h. Microarray washings were performed as recommended by Agilent and slides were scanned at a 3-mm resolution using a Surescan microarray scanner (Agilent Technologies, Palo Alto, CA).
Pixel intensities were extracted using the ''Feature Extraction'' software (Agilent Technologies, Palo Alto, CA). The retained intensity value for each probe was the spot's median intensity signal. For each probe, the median value of its replicates was calculated and was further identified as the ''probe signal''. For each of the 5 regions (considering every bacterial family), the highest probe signal was selected as the more representative probe and characterized the ''region signal''. For each family, a mean signal of the five ''region signals'' was calculated providing the ''family signal''. It was then used to determine the relative abundance of each family by dividing it with the sum of all the ''family signals''. Specific scripts developed in this study with the Delphi and the C++ languages were used to automatically perform these data extractions.