dataesr/scientific-paragraphs-categorization huggingface dataset:
OUD Characteristics The evaluation was performed on a single time point, but the patients were questioned on two distinct periods: before the OST initiation and since the OST initiation. Before the OST initiation. We collected data including the age at first opioid experimentation, age at opioid dependence, age at the first attempt to stop opioid use (withdrawal or OST), main substance used (heroin, nonmedical use of codeine, morphine, buprenorphine, and methadone), main route of administration (nasal, intravenous, inhaled, oral), and negative consequences related to opioid dependence (financial, socioaffective, psychiatric, professional, legal, physical problems). Since the OST initiation. We collected the characteristics of OST (type of medication, duration, OST initiation with daily supervised dosing by a qualified health professional, maximal daily dose, current daily dose, compliance, withdrawal symptoms, current opioid use despite OST or opioid abstinence, defined as the self-reported absence of opioid use over the previous 6 months).
20 criterion and subsequently selected in the model using the P < 0. 05 criterion). The corresponding odds ratio and associated 95% confidence interval were estimated. The ability of the final logistic model to discriminate between successful or unsuccessful OUDT was assessed using the area under the receiver operating characteristic (ROC) curve, and the goodness of fit of the model was assessed using the Hosmer-Lemeshow test. The statistical analyses were carried out with SAS 9. 1 and R statistical software (SAS Institute, Inc. ).
There is a pervasive assumption that elections can be won and lost on the basis of which candidate or party has the better data on the preferences and behaviour of the electorate. But there are myths and realities about data-driven elections. It is time to assess the actual implications of data-driven elections in the light of the Facebook/Cambridge Analytica scandal, and to reconsider the broader terms of the international debate. Political micro-targeting, and the voter analytics upon which it is based, are essentially forms of surveillance. We know a lot about how surveillance harms democratic values. We know a lot less, however, about how surveillance spreads as a result of democratic practices -by the agents and organisations that encourage us to vote (or not vote). The articles in this collection, developed out of a workshop hosted by the Office of the Information and Privacy Commissioner for British Columbia in April 2019, address the most central issues about data-driven elections, and particularly the impact of US social media platforms on local political institutions and cultures.
Those taking this view contend that the commodification of a mass of personal data, gathered and sorted from largely unwitting users, has now become a dominant mode of accumulation. The classification of those data enables their use in multiple settings, including the present context of elections. The Cambridge Analytica scandal would not have been such without Facebook, for which both 'prediction' and 'personalisation' are central. We have known about the potential for Facebook to engage in "digital gerrymandering" for several years  (Zittrain, 2014). Contemporary surveillance has several features that resonate with questions raised by data-driven elections. It sorts populations into groups so that they may be treated differently, which is often divisive in its effects. It assumes that classificatory algorithms work effectively to encapsulate user opinions, thus questioning users' self-positioning.
In a recent report  (Tactical Tech, 2019) , analysed in the commentary below by Varoon  Bashyakarla (2019) , the Tactical Tech collective has portrayed the extensive contemporary political "influence industry". Bashyakarla's commentary makes a useful distinction between data as a political asset (through traditional databases or voter relationship management systems), as political intelligence (through constant A/B testing and experimentation), and as political influence (through microtargeting techniques). It documents the range of companies, consultancies, agencies and marketing firms, from local start-ups to global strategists, that target parties and campaigns across the political spectrum, often with militaristic rhetoric -"we win the tough fights", "we power democracy", "ignite your cause", "your revolution starts here" (Tactical Tech, 2019).
Bashyakarla contends that the question "does this targeting work" reflects a short-sighted obsession with winning, and misses the far larger point about the effect of the weaponisation of personal data on the larger democratic infrastructure. The work of Jeff Chester and Kathryn Montgomery has traced the ongoing "marriage of politics and commerce" and the growth of data-driven political marketing  (Chester & Montgomery, 2017). They reviewed seven key techniques employed during the 2016 campaigns in the US, all of which point to massive efforts at consolidation in the digital marketing ecosystem: crossdevice targeting; programmatic advertising; lookalike modelling, such as that offered through Facebook; online video advertising; targeted TV advertising; and psychographic, neuromarketing and emotion-based targeting.
ACKNOWLEDGEMENTS On behalf of the  Big Data Surveillance  project, we would like to thank the Information and  Privacy Commissioner  for BC,  Michael McEvoy , for hosting the workshop from which the papers in this issue were derived, and to his staff, particularly  Jeannette Van Den Bulk  and  Nathan Eliot. We also appreciate the assistance of  Michael McDonald  and  Jesse Gordon , graduate students at the  University of Victoria , and  Emily Smith  of the Surveillance Project. We also acknowledge the very helpful and prompt work of managing editor  Frédéric Dubois  and student assistant Patrick Urs Riechert in getting this issue reviewed and published in a timely fashion.
Introduction In most countries, road safety performance has traditionally been measured by the reduction of fatalities. The total number of road fatalities in Europe declined by 42% overall between 2000 and 2013 within the 32 countries in the International Road Traffic and Accident Database (IRTAD) for which data are consistently available  (Road Safety Annual Report 2015 , 2015). However, crashes also cause numerous serious road injuries, resulting in considerable economic and human costs. For the first time, in 2016, the European Commission published a global estimate for the number of people seriously injured on Europe's roads: 135,000 in 2014  (European Commission, 2016). It has been estimated that in some countries the number of serious road injuries has not been decreasing as fast as the number of fatalities, while in other countries the number of serious road injuries has even been increasing  (Berecki-Gisolf et al.
An AIS-Severity Code of 6 is not the arbitrary code for a deceased patient or fatal injury, but the code for injuries specifically assigned an AIS 6 severity. An AIS-Code of 9 is used to describe injuries for which not enough information is available for assessing its severity. The AIS scale is a measurement tool for single injuries. The MAIS (Maximum AIS) is the maximum of the AIS scores for each region of the body, and is frequently used for assessing overall severity. It does not necessary have a linear relationship with the probability of death. AIS can be coded directly or can be derived from other injury coding systems, like the International Classification of Diseases in its several versions (ICD, ICD9-CM, ICD10, etc). When using large databases, usually hospital databases, it is likely that AIS is not coded directly but derived using a convertor algorithm.
Various conversion tools are available for recoding ICD codes into AIS codes: ICDmap90, 1 ICDpic, 2 DGT, 3 ECIP, 4 AGU 5 or AAAM. 6 The use of any of these conversions tools leads to the so-called ICD-derived AIS values. Some of these tools recode the ICD codes into the latest AIS© 2005/update 2008 codes, but other older tools recode ICD data into AIS codes that are based on previous versions of the AIS coding (AIS2005, AIS1998 or AIS1990)  (Ringdal et al. , 2012). Recoding always has the disadvantage compared to direct coding, that some information gets lost or is not available so that a best match must be selected (in the recoding tool). This may influence the severity that is assigned to a casualty and therefore also on the estimated number of MAIS3 + casualties. However, there is very limited amount of literature comparing directly coded (M)AIS levels to ICD-derived (M)AIS levels, derived using recoding tools  (Bartolomeo et al.
Data and method In this section, we describe the methods used to identify the inclusion and exclusion criteria and the factors that affect obtaining MAIS3 + through conversion tools. The paper addresses the two objectives in two separate sections. Firstly, we explore the effect of in/ exclusion criteria using data from Spain and the Netherlands. Secondly we analyse the impact of using different conversion tools to derive MAIS3 + using data from Austria (AT), Belgium (BE), France (FR), Germany (GE), Netherlands (NL) and Spain (ES).
Inclusion and exclusion criteria A descriptive analysis was carried out to examine the effect of certain choices concerning in-and exclusion criteria based on codes of the International Classification of Diseases/Injuries (ICD9-CM, ICD10) with the aim to arrive at a common consensus as to which codes should be used to report road traffic serious injuries. The main parameters explored were: • What to do with deaths before and after 30 days • Whether scheduled admissions should be included • Whether readmissions should be included • How to treat hospitalisations of 1-day treatment or less   In addition, as in many countries, frequently the availability of data does not allow identification of all specific criteria to select cases from hospital data. A sensitivity analysis has been carried out with the aim of deriving weighting factors to use when no data is available.
Weighting factors were calculated based on the effect resulting from including/ excluding each option. A descriptive analysis was carried out for each parameter using hospital data from Spain and The Netherlands separately. In Spain, two data sources were used. First, the Spanish Database from Hospital Discharge Register (HDR) for 2011 was used to analyse criteria based on ICD9-CM. This database includes all hospitalisations for any injury (traffic and non-traffic) in Spain from all public hospitals and around 99% of private hospitals. The population of study is whole the Spanish population. The unit of analysis of the database are admissions, not individuals. To identify individuals a personal identification code is used. The criteria for defining a readmission can vary across countries. In Spain, if the same person has different admissions due to the same reason in the same hospital or another within less than 30 days of difference, they are classified as re-admissions (of the same individual).
Due to data protection, frequently it is not possible to identify number of visits of the same person, therefore we need to rely on the hospital definition. In the same sense hospital admissions can take place through emergency attendance or be scheduled. These scheduled admissions may be a second episode of a previous emergency injury or may not, so we also studied them in the analyses. Spain was coding diseases and injuries using the ICD9-CM up until December 2015 when the process of coding changed to ICD10. The database includes up to 14 diagnoses (including diagnosis of injuries, diseases and codes of external causes). To analyse criteria based on ICD10, we used data from the National Register of Mortality for the years 2009-2013. We included cases with ICD10 codes V01 to V99. Spain has coded mortality using ICD10 since the year 2000.
In order to identify road traffic injuries in hospital data it is necessary to know the police definition. The Spanish General Directorate of Traffic (DGT) in accordance with Eurostat defines a 'Road traffic collision with victims' as "collision occurring or starting on a road which is the object of motor vehicle traffic and road safety legislation (public road), involving at least one vehicle in motion, and which results in the death and/or injury of one or more people". According to the ICD9-CM the definition of road traffic injuries includes any traumatic injury including codes from 800 to 959. We will analyse the impact of including late effects of injury and complications of physical trauma (905 to 909, 958 and 959). In addition, external codes E810-819 ("Motor vehicle traffic accident") are clearly relevant to identifying road traffic casualties.
Traffic injuries were defined as those injuries meeting either criteria 1 or criteria 2: 1 E-code for external cause (ICD9-CM): E810-819, E826-829, E929, E988. 5. 2 Road Accident compensation payer. Police registers include road traffic fatalities as those up to 30 days after the collision. Therefore, in hospital databases it is important to ensure that fatalities are not double counted (as injured and as fatality), and would produce an overestimation. This means that if a person is admitted to hospital but finally dies within 30 days after the admission they should be counted as a fatality (as in the police registers). However, if the person dies after 30 days, they should be recorded as injured according to their severity, since they are not included in the fatalities statistics. For ICD9-CM, AIS and MAIS were derived using the icdpic module of Stata ("STATA Data Analysis and Statistical Software," n.
d. ) from the 14 diagnoses reported in the Spanish Hospital Discharge Database for 2011. For ICD10 it has not been possible to derive MAIS (apart from all fatal cases) because there is no information in the data set about injuries. There is only information about the external cause of injury. Therefore, we used this information to show the distribution of ICD10 codes stratified by traffic and non-traffic, in order to establish criteria for inclusion. In the Netherlands, a selection of the Dutch Database from the Hospital Discharge Register (HDR) for 1993-2013 was used. Dutch hospitals code injuries with the ICD9-CM. The database includes up to 10 diagnoses  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  including diagnosis of injuries, diseases and codes of external causes).
After 2012, hospitals gradually introduced ICD10 for coding. In this study only the ICD9-CM cases of 2012 and 2013 have been considered. For this analysis, all ICD10 codes have been converted to ICD9-CM and treated likewise. The Dutch Hospital Data includes all hospitalisations for patients with a transport accident as the external cause (E-codes in: E800-E848), extended with a wide range of external causes where -in case of miscoding-other traffic casualties can be found (E-codes in: E880-E889 (falls), E890-E899 (burns), E928 (unknown accidental), E929 (late effects), E958 (suicide), E988 (unspecified)). All hospitals are included in the database. For some of the years of interest some hospitals did not provide data, but because the number of patients treated is known an accurate estimate of the missing data can be given.
The population of study is complete the Dutch population, including all persons -irrespective of their nationality-having an accident in the Netherlands who are admitted to a Dutch hospital. At a first stage, the unit of analysis of the database is admissions, not casualties of (traffic) incidents. Therefore, as with the Spanish data, it is necessary to identify the individuals before any analysis can be completed using the patient number. AIS and MAIS were derived from the icdmap90 module of Johns Hopkins University (1998). In the Netherlands, for this study, to examine the sensitivity for the Adapted from  (Smith et al. , 1991). inclusion criteria the following definition was used: all severities on the MAIS scale, including deaths, and all patients with an E-code in the range E810-E829 + linked cases for E929.
However, in practice this is often not possible. Using ICD conversion tools is often necessary to derive AIS, but, the combination of differing ICD conversion tools and various versions of the coding instructions may result in variations in the estimated number of MAIS3 + casualties. In a similar way, including a limited number of injuries and the use of truncated injury codes has the potential to affect the estimated casualties. We studied these effects using hospital data from Austria (AT), Belgium (BE), France (FR), Germany (DE), Netherlands (NL), and Spain (ES). The implications for each of the factors (1-4) listed above were considered in relation to the following: a The effect of different AIS versions was studied using the GIDAS (DE) in-depth accident survey data set of accidents recorded since 2008 that have been coded in both AIS1998 and AIS2008.
b To study the effect of direct coded AIS compared to ICD-derived AIS, a dataset from Germany (GIDAS) was available where AIS-codes and independent ICD10 codes were available for the same RTC (Road Traffic Casualty). ICD10 codes were recoded with AAAM10, ECIP and AGU to rate the severity and compared to the direct AIS. Unfortunately, no other datasets were available to study other combinations of AIS versions. c The effect of different mapping tools was studied with data from NL, ES, BE where the ICD-codes were recoded to AIS with different tools: ICDmap90, DGT, ICDpic, AAAM9, ECIP and AAAM10. d The effect of using a limited number of injuries for the MAIS calculation was investigated by studying the effect on the estimated number of MAIS3 + casualties in case of leaving out injuries (NL, BE, ES).
Eighteen fatalities that died after 30 days of admission had slight or moderate injuries (MAIS = 0-2) (Table  1 ). (Obviously, there were fatalities who died at the scene and were not admitted to hospital). From this point and for the purposes of this study, deaths within 30 days (n = 526) are excluded for the sensitivity analyses. Deaths after 30 days are distributed according to their MAIS. Therefore, the database includes 30,813 admissions (Table  1 ). According to data showed in Table  2 , if re-admissions are excluded, the percentage of severe road traffic injuries is similar (29. 0% versus 29. 4%). The percentage increases when scheduled admissions are excluded (from 29. 0% to 33. 8%). This is mainly due to the fact that the total number of road traffic injuries dropped from 30,813 to 24,761.
Direct coding versus various conversion tools Directly coded AIS levels were compared with ICD derived AIS levels, different ICD recoding tools and different AIS versions were considered. The results of those analyses are summarised in Table  5. In the German GIDAS database, 16,695 casualties were coded in both AIS1998 and AIS2008. Line 1 in Table  5  shows that the AIS1998 version results in roughly 12% more MAIS3 + casualties compared to the AIS2008 version. Some of the data from the German GIDAS database could be matched to patient information from the Medical School in Hannover. For 209 trauma casualties, ICD10GM (ICD10 German modification) injury codes were available and recoded to AIS using various ICD10 recoding tools. Line 2 of Table  5  shows that using ECIP results in a 13% higher number of MAIS3 + casualties compared to direct coding, whereas both AAAM10 and AGU result in an underestimation.
The way scheduled admissions (contrary to acute admissions) are treated can also vary from one country to the other. If a scheduled admission is interpreted as the second medical examination after a first visit to the hospital (probably in the accident & emergency department) and the diagnosis information is specific enough to identify a MAIS3 + injury and this case is present in the hospital data file, then the scheduled admission can be left out as information of the first encounter is present. If re-admissions can be excluded from the database (as these are explicitly recorded or can be identified in a de-duplication process), it is not necessary to exclude scheduled admissions as the  process of de-duplication takes care of that. If re-admissions are not specifically recorded, as in the case of Spain, then scheduled admissions they should be also excluded as they are likely to include many readmissions.
Ideally MAIS3 + casualties that are not admitted to the hospital should also be taken into account as a serious road injury. However, as in most countries, hospital discharge data is the only database available to estimate the number of serious road injuries, it is not possible to include non-hospitalised casualties. The Rhone register in France estimates that exclusion of non-hospitalised MAIS3 + casualties results in an underestimation of the number of serious road injuries of roughly 5%. The actual inclusion or not of outpatients within the definition of serious road traffic injuries varies depending on the country; however, it is convenient to include them in the definition all traffic injury hospitalisations, even if they only generate short stays in the hospital. In the case of the inclusion of late effects of injury and complications of physical trauma  (905-909, 958 and 959)  it should be noted that they automatically are excluded when obtaining severity.
Since 2008 this percentage has always been lower than 20% and in 2010 this figure was no more than 16%. This percentage covers all patients visiting a hospital because of an external cause. It is uncertain to what extent this overall percentage can be extrapolated to the whole subgroup of road victims. In Spain, 17% of records are missing the E-code. Due to the lack of a complete database with E-codes it is not possible to derive a weighting factor adjusting for missing E-codes. Some countries look for other variables to identify traffic injury cases. In Spain for example, "compensation Payer Company" is used as an additional variable to select road traffic casualties. The use of this variable enabled identifying 25% of all road traffic casualties that were not identified by the selection of E-codes.
However, ICDpic performs quite well in classifying AIS3 + injuries for each body region. Greene et al conclude that ICDpic may be a preferred tool in determining injury severity for large trauma datasets, but caution needs to be taken when examining smaller trauma sets. AAAM convertors were developed by the Association for the Advancement of Automotive Medicine for the European Commission, and are the official tool recommended and freely distributed by DG MOVE (Directorate-General for Mobility and Transport). The tool provides for any specific diagnosis whether its MAIS is < or ≥ 3. The European Commission acknowledges that the AIS (in all its versions) is the property of the Association for the Advancement of Automotive Medicine (AAAM), owner of the Copyright  (European Commission, 2013).
Results from our study show that AAAM for ICD9-CM provides acceptable figures, while for ICD10 results in an underestimation of number of serious injuries. The consequences of truncated injury codes and of including a limited number of injuries when determining MAIS were investigated using data from Spain, Belgium and the Netherlands. The effect of truncation appears to depend on the recoding tool that is applied; AAAM10 and ICDpic do not seem able to deal with truncated codes. ICDpic shows a large decrease in the number of MAIS3 + casualties when injuries are truncated while the AAAM10 tool shows a considerable increase in the number of MAIS3 + casualties. We recommend repeating this analysis when the conversion tables are adapted for the European needs to have a closer look at how the tool deals with truncated codes.