dataesr/scientific-paragraphs-categorization huggingface dataset:
EGB is a completely anonymized database with no possible interaction with the persons contributing information. The identification of persons included in the EGB sample is protected by an anonymization process with two cryptographic levels, a procedure conforming to French data protection legislation. The use of EGB for medical research has been approved by the National Commission for Informatics and Liberties (CNIL). The conditions of use of EGB for research purposes and the authorized accesses are stated by law. Researchers involved in this study were authorized to use the database, and have been trained in its use. The study were declared to and approved by the National Institute of Medical Research and Statistics (INSERM).
Patient selection and classification Patients were included in the treated T2DM cohort if the antidiabetics (ADs) or insulin were dispensed for more than 80% of days over 15-months during the follow-up period. Patients with any evidence of pregnancy, gestational diabetes or any other type of diabetes during the treatment period were excluded. In absence of complete diagnostic information in the database, patients with declining renal function (DRF) were identified as patients with at least one of the     For each patient, only the years during which the patient was constantly in the same treatment phase were considered. Each year, patients were excluded if they were lost to follow-up (no healthcare resource use during 3 months during the calendar year) or discontinued T2DM treatment. The estimation of T2DM-attributable expenditures was based on the difference in expenditures between patients with T2DM and matched controls without diabetes.
8/week) was the most important contributor to the costs of insulin-therapy (€45. 4/week). Real-world healthcare expenditures were measured in this study by analyzing health insurance claims data. The strengths of the EGB database, relative to other health insurance databases in Western countries, include its size, its representativity, the absence of selection according to clinical or socioeconomic criteria, and the fact that most persons are continuously enrolled. However, this study has limitations related to the utilisation of administrative data, rather than clinical data. Thus, the identification of patients with DRF was based on very restrictive assumptions. We estimated the proportion of patients with DRF at 10%, whereas higher estimates have been reported  [10, 11]. This implies that the groups of patients with NRF probably included a few patients with DRF.
The EGB database contains no other socio-demographic data than age, sex, date of death, residence department and the affiliation in the CMU. Education level, income level or occupational category of beneficiaries, for which the influence on health care consumption has been demonstrated  [12, 13] , are absent from the EGB. This information should be collected through specific surveys. The description of the results over time does not consider all treated DM patients but only focuses on the subpopulation of DM patients that are stable in their treatment. Patients not stable or switching from a treatment to another within the year were excluded from the analysis. This facilitated the comparison of expenditures between treatment stages but also introduced a selection bias that might affect the description of expenditures for the overall population.
They show that commodity correlations have increased since 2003, limiting hedging substitutability in portfolios. Our study extends the previous literature by considerably enlarging the sample of commodities analyzed. We consider 25 different strategic commodities, traded in the US and covering various sectors: energy, precious metals, agricultural, non-ferrous metals, food, oleaginous, exotic and livestock. The dataset we have built allows us to compare the behavior of each commodity group regarding stock market fluctuations, and to study whether correlations between commodities and equities evolve over time and depend on the situation-bearish or bullish-on the stock market.
DATA AND STYLIZED FACTS We consider daily spot price series extracted from Datastream for a large sample of commodities over the January 3, 2001 -November 28, 2011 period. 9 We investigate 25 different commodities covering the following various sectors: energy, precious metals, agricultural, non-ferrous metals, food, oleaginous, exotic and livestock. All price series are quoted in US dollars. We also consider an aggregate commodity price index, the Commodity Research Bureau (CRB) index. Regarding the equity market, we rely on one of the main US stock market index, namely the S&P 500 index. Table  1  in Appendix provides some descriptive statistics regarding the returns series, defined as r t = ln(P t /P t-1 ), where P t denotes the price index at time t. The group of energy commodities seems to differ from other groups in terms of volatility: the variance of electricity, gas and to a lesser extent oil price returns is higher than that obtained for the other commodities; 10  being also higher than those of S&P 500 and CRB returns.
The cross country demand analyses conducted with the ICP data have all divided total consumer expenditures into nine consumption  categories: food, clothing and footwear, education, housing, house furnishings and operations, medical care, transport and communications, recreation, and other expenditures. The food category, which only included food consumed at home (including beverages and tobacco) in the earlier ICP surveys, also includes food consumed away from home in the 2005 ICP. Using the 1996 and the 2005 ICP data, two of the studies  (Seale et al. , 2003; Muhammad et al. , 2011)  conduct the second stage analysis as well, focusing on the eight food subgroups: cereals (including bread), meat, fish, dairy products, fruits and vegetables, oils and fats, beverages and tobacco, and other food products.
Access to the French drug market is being impacted by an ongoing dramatic shift in practice as well as by two laws that came into force in December 2011. This new environment has been described and analyzed in two separate articles. The first article aims to describe the recent changes in access to the French drug market. The severity of the condition being treated, which used to be the main determinant of the drug's reimbursement level in France, has now been replaced with the drugs' efficacy criterion. Moreover, the effect size required for acknowledging drug innovation has substantially increased. Perceived evidence might also be more important than actual evidence. Comparative evidence and real-world data are considered critical conditions for marketing authorization. Cost-effectiveness studies will now be part of the market access requirements for all drugs in order to satisfy the selection criteria for medico-economic assessment.
Methods and Findings: We searched ClinicalTrials. gov on March 27, 2012, for randomized controlled trials of drugs with posted results. For a random sample of these trials, we searched PubMed for corresponding publications. Data were extracted independently from ClinicalTrials. gov and from the published articles for trials with results both posted and published. We assessed the time to first public posting or publishing of results and compared the completeness of results posted at ClinicalTrials. gov versus published in journal articles. Completeness was defined as the reporting of all key elements, according to three experts, for the flow of participants, efficacy results, adverse events, and serious adverse events (e. g. , for adverse events, reporting of the number of adverse events per arm, without restriction to statistically significant differences between arms for all randomized patients or for those who received at least one treatment dose).
From the 600 trials with results posted at ClinicalTrials. gov, we randomly sampled 50% (n = 297) had no corresponding published article. For trials with both posted and published results (n = 202), the median time between primary completion date and first results publicly posted was 19 mo (first quartile = 14, third quartile = 30 mo), and the median time between primary completion date and journal publication was 21 mo (first quartile = 14, third quartile = 28 mo). Reporting was significantly more complete at ClinicalTrials. gov than in the published article for the flow of participants (64% versus 48% of trials, p,0. 001), efficacy results (79% versus 69%, p = 0. 02), adverse events (73% versus 45%, p,0. 001), and serious adverse events (99% versus 63%, p,0. 001). The main study limitation was that we considered only the publication describing the results for the primary outcomes.
This is the case, for example, when the difference in means between treatments is reported but not a measure of precision. To overcome these issues, the 2007 US Food and Drug Administration Amendments Act (FDAAA) requires that the results from clinical trials of Food and Drug Administrationapproved drugs and devices conducted in the United States must be made publicly available at ClinicalTrials. gov within 1 y of the completion of the trial, whether the results are published or not  [15] [16] [17] [18]. This US public law requires a ''table of the demographic and baseline data collected overall and for each arm of the clinical trial to describe the patients who participated in the clinical trial …  [and]  a table of values for each of the primary and secondary outcome measures for each arm of the clinical trial, including the results of scientifically appropriate tests of the statistical significance of such outcome measures.
Search for Trials with Results Posted at ClinicalTrials. gov We searched ClinicalTrials. gov on March 27, 2012, using the following keywords: ''Closed study'' in the Recruitment field, ''With results'' for Study Results, ''Interventional studies'' for Study Type, and ''Phase III and IV'' for Phase. We selected completed phase III or IV randomized controlled trials listing only drugs as intervention type. We excluded trials comparing a drug to a device. We excluded phase II/III trials, considering them to be phase II trials. Of all eligible trials (n = 1,592), we selected a random sample of 600 trials for which to search for full-text publications.
Search for Publication of Results in Journals Whenever possible, we used the link within ClinicalTrials. gov to identify the published article. We also systematically searched MEDLINE via PubMed by using the ClinicalTrials. gov identification number (NCT number). If no publication was identified, we searched MEDLINE via PubMed again by using keywords for drug names and the condition studied. The articles identified through the search had to match the corresponding trial in terms of the information registered at ClinicalTrials. gov (i. e. , same objective, same sample size, same primary outcome, same location, same responsible party, same trial phase, and same sponsor) and had to present results for the primary outcome. A second reviewer checked the matching between ClinicalTrials. gov and the published article.
Data Extraction We collected the following information from ClinicalTrials. gov for the random sample of 600 trials with results posted at ClinicalTrials. gov: 1. General characteristics of the trial: primary funding sources (extracted from Study Sponsor at ClinicalTrials. gov), medical specialty (extracted from Conditions at ClinicalTrials. gov), and countries where the trial was conducted (extracted from Location Countries at ClinicalTrials. gov). We also collected trial primary completion date (defined as the date of final collection of data for the primary outcome) and date when results were first publicly posted-this date was extracted from the archive record and differs from the date on which results were first received, which is available under Study Results at ClinicalTrials. gov. The difference between these two dates is related to production and the vetting of the results by the US National Institutes of Health.
Interventions: details concerning interventions for experimen- tal and control groups were extracted from Study Arm(s) at ClinicalTrials. gov. Then, for all trials with both results posted and published, we collected the following information independently from Clinical Trials. gov and from the published article: 1. Flow of participants in the trial: reporting of the flow of participants, including number of participants assessed for eligibility, number of participants randomized overall and per arm, number of participants who received the intervention per arm and whether the reasons for not receiving the intervention were specified, number of patients lost to follow-up and those who discontinued the intervention and whether the reasons for discontinuation were given, and number of participants analyzed per arm and whether the reasons for excluding participants from the analysis were reported.
2. Efficacy results: for the primary outcome posted at ClinicalTrials. gov, we extracted data from ClinicalTrials. gov for this outcome. If several primary outcomes were posted at ClinicalTrials. gov, we focused on the first registered. When we extracted efficacy results in the published article, we extracted data for this outcome whether it was reported as primary or secondary. If the outcome was not reported at all, we considered the data to be missing. For all types of outcomes, we collected whether numbers of patients analyzed per arm were reported. For binary outcomes, we collected whether the number of events per arm was reported. For continuous outcomes, we collected whether (1) mean (6 standard deviation [SD]) or (  2 ) median (interquartile) was reported or (3) neither of these was reported.
Assessment of the Completeness of Reporting Three experts in clinical epidemiology reached consensus on the main elements that needed to be reported for each of the following domains: the flow of participants during the trial, efficacy results, adverse events, and serious adverse events, on the basis of data required to perform meta-analyses. The reporting was considered complete for each domain if all of the included elements in Box 1 were reported and incomplete if one or more elements were missing from the items listed in Box 1. In a second step, we compared the number of elements reported at ClinicalTrials. gov and in the published article for the flow of participants, efficacy results, adverse events, and serious adverse events. We assessed the number of pairs (with percentage) for which ClinicalTrials.
Statistical Analyses Inter-rater agreement between the two reviewers was assessed by the Kappa coefficient (95% CI). Descriptive analyses of trial characteristics included numbers and percentages. Time from trial primary completion date to the date of posting of results at ClinicalTrials. gov or online publication in journals was described with the Kaplan and Meier method for trials with results both posted at ClinicalTrials. gov and published. We compared results posted at ClinicalTrials. gov and in the published article for completeness of reporting using McNemar's test of equality of paired proportions. We tested the interaction between completeness of reporting at ClinicalTrials. gov and in the published article and the following trial characteristics: type of journal (general versus specialty), type of control (active versus placebo or no treatment), source of funding (academic, industry, both), and study design (parallel arms versus crossover) using a generalized estimation equation (GEE) model to take into account the correlation between the paired observations.
Time to Posting Results at ClinicalTrials. gov and Publication in Journals Of the 600 trials with results posted at ClinicalTrials. gov that were randomly selected for a search for corresponding publications, we excluded five that were not randomized controlled trials, and one that was still enrolling patients. From the remaining 594 trials, 297 (50%) had no corresponding published article. The median year of completion of trials with no corresponding article was 2009 (first quartile [Q1] = 2008, third quartile [Q3] = 2009). We included 202 pairs of reports for publicly posted and published results. In total, 18 of the 202 trials (9%) had multiple publications (from two to 14). These reports concerned protocols or.
Serious adverse events Number of serious adverse events per arm preliminary results such as baseline results (n = 6), interim analyses (n = 3), long-term outcomes (n = 15), other outcomes (n = 19), and/ or other results (n = 1). None of the eliminated reports contained additional safety data for the same time frame as the selected report. Two of the eliminated reports contained additional safety results for longer-term follow-up. The median time between primary completion date and results first being publicly posted at ClinicalTrials. gov was 19 mo (Q1 = 14, Q3 = 30 mo); the median time between primary completion date and publication in journals was 21 mo (Q1 = 14, Q3 = 28 mo) (Figure  2 ).
Reporting of Flow of Participants For the 202 trials with both publicly posted and published results, the number of patients assessed for eligibility was reported for 1% (2/202) of trials at ClinicalTrials. gov and for 65% (131/ 202) in the published article (Table  2 ). The number of patients allocated to the intervention group was reported for 99% (200/ 202) of trials at ClinicalTrials. gov and for 95% (192/202) in the published article. The number of patients lost to follow-up per group was reported for 66% (133/202) of trials at ClinicalTrials. gov and for 53% (108/202) in the published article. The number of patients analyzed was reported for 96% (193/202) of trials at ClinicalTrials. gov and for 88% (177/202) in the published article. The reasons for participant exclusion from the analysis were reported for only 8% of trials (16/202) at ClinicalTrials.
Reporting of Efficacy Results For trials with a binary outcome (n = 73), the number of patients analyzed was reported for 97% (71/73) of trials at ClinicalTrials. gov and for 89% (65/73) in the published article (Table  3 ). The number of events was reported for 55% (40/73) of trials at ClinicalTrials. gov and for 66% (48/73) in the published article. For trials with a continuous outcome (n = 107), the mean or median was reported for 100% (107/107) of trials at ClinicalTrials. gov and for 90% (96/107) in the published article (Table  3 ). Dispersion was reported for 96% (103/107) of trials at Clinical Trials. gov and for 64% (69/107) in the published article. For trials with a time-to-event outcome (n = 22), the median time to event was reported for 50%  (11/22 ) of trials at ClinicalTrials.
Reporting of Adverse Events For the 202 pairs of trial reports, the population for analysis corresponded to all randomized participants for 57% (115/202) of trials at ClinicalTrials. gov and 36% (72/202) of published articles (Table  4 ). The total number of adverse events was reported for 96% (194/202) of trials at ClinicalTrials. gov and for 63% (128/ 202) in the published article. All adverse events per arm were reported for 13% (26/202) of trials at ClinicalTrials. gov and for 5% (10/202) in the published article. Otherwise, reporting was restricted to the most common events for 99% (174/176) of trials at ClinicalTrials. gov and for 44% (85/192) in the published article, or to statistically significant events for 15% (29/192) of trials in the published article. Withdrawals due to adverse events were reported for 80% (161/202) of trials in ClinicalTrials.
Completeness of Reporting For the 202 pairs of trial reports, the proportion of trials with complete reporting was significantly higher at ClinicalTrials. gov than in the published article for the flow of participants (64%  [   5 ). We found statistically significant interactions between completeness of reporting of adverse events and type of journal (74% and 38%, respectively, in ClinicalTrials. gov and in the published article for trials published in a specialty journal versus 70% and 59%, respectively, for trials published in a general journal, p for interaction = 0. 015) as well as source of funding (75% and 5%, respectively, in ClinicalTrials. gov and in the published article for trials with academic funding; 73% and 50%, respectively, for trials with industry funding; and 56% and 33%, respectively, for trials with academic and industry funding, p for interaction = 0.
01). There was more information reported at ClinicalTrials. gov than in the published article for 29% (59/202) of trials for the flow of participants, 21% (42/202) for efficacy results, 40% (80/202) for adverse events, and 36% (73/202) for serious adverse events. There was less information in ClinicalTrials. gov than in the published article for 10% (21/202) of trials for the flow of participants, 10% (21/202) for efficacy results, 12% (24/202) for adverse events, and 1% (1/202) for serious adverse events of trials (Table  6 ).
Discussion To our knowledge, this is the first study comparing the timing and completeness of trial results publicly posted at ClinicalTrials. For trials with results both posted at ClinicalTrials. gov and published in journals, the median time to results being first publicly posted and published was 19 mo (Q1 = 14, Q3 = 30 mo) and 21 mo (Q1 = 14, Q3 = 28 mo), respectively, and the completeness of results was significantly better at ClinicalTrials. gov than in the corresponding published articles. In particular, serious adverse events were almost always reported at ClinicalTrials. gov. A previous study assessed trial publication for completed trials registered at ClinicalTrials. gov and showed that fewer than half were published  [20]. Other studies evaluated the quality of reporting of the World Health Organization minimum dataset in ClinicalTrials.
gov [21]. A recent study published in 2012  [22]  compared the quality of reporting among registry reports, clinical study reports submitted to regulatory authorities, and journal publications. The authors identified only industry registry reports, with no trials being registered in a public registry. They concluded that industry registry reports and journal publications insufficiently reported the results of clinical trials but may supplement each other. With the FDAAA requiring mandatory posting of results within 1 y after the primary completion date  [15] [16] [17]  and standardized reporting of results  [17] , Clinical-Trials. gov has become an interesting source of data for assessing trial results.
Actually, about half of the trials with results posted at ClinicalTrials. gov did not have published results. Further, our results highlight the need to assess trial results systematically from both ClinicalTrials. gov and the published article when available. Based on our results, searching Clinical Trials. gov is necessary for all published and unpublished trials to obtain more complete data and to identify inconsistencies or discrepancies between the publicly posted results and the publication. As outlined by Zarin et al. , ClinicalTrials. gov is designed to complement, not replace, journal publication  [28]. Nevertheless, not all trials have their results posted at ClinicalTrials. gov. Some studies previously showed low compliance with the FDAAA regarding mandatory posting of results at ClinicalTrials.
gov [29] [30] [31] [32]. Moreover, this law concerns trials performed in the US, with no similar law in Europe or elsewhere. Our results also highlight the role of trial registries for researchers and methodologists exploring publication bias and selective reporting of outcomes. For example, researchers could use trial registries to assess whether studies with a significant main outcome are more likely to be published or published more quickly than those with a negative outcome using data recorded in registers. For peer reviewers, our results emphasize the important role of trial registration during the peer-review process. Actually, reviewers and academic editors could assess whether all safety events, especially serious adverse events, are fully reported in the submitted articles. In our study, serious adverse events were reported for 99% of trials at ClinicalTrials.