dataesr/scientific-paragraphs-categorization huggingface dataset:
PANELIST BIOS Prof. Dr. Jérôme Darmont is full professor of computer science at the University of Lyon 2, France, and the director of the ERIC laboratory. He received his Ph. D. in 1999 from the University of Clermont-Ferrand II, France, and then joined the University of Lyon 2 as an associate professor. He became full professor in 2008. His research interests mainly relate to database and data warehouse performance (performance optimization, auto-administration, benchmarking. ) and cloud Business Intelligence (data security, query performance and cost, personal BI. ). He has published more than 100 peer-reviewed papers. He is a member of several editorial boards and has served as a reviewer for numerous conferences and journals. Along with Torben Bach Pedersen, he initiated the Cloud Intelligence workshop series in 2012.
Data extraction We created a modified version of the CONSORT checklist which contained all of the 2008 CONSORT-NPT checklist items and all of the standard 2010 CONSORT checklist items. The resulting checklist had a total of 42 items (see online supplementary appendix S3). Two authors (DH and WT) independently assessed each of the eligible reports against this checklist. Reports were also scored by the same authors for trial quality using the extended version of the Linde Internal Validity Scale (ELIVS) (see table  3 , online supplementary appendix S2). The ELIVS scoring system used in this study was developed from initial work by Jadad et al  23  and Linde et al. 24  It measures the following quality domains: treatment allocation, randomisation method, allocation concealment, postrandomisation baseline comparison, blinding, handling and reporting of withdrawals and intention to treat analysis.
Any discrepancies were resolved by consensus. Interobserver analysis was assessed by calculating the Cohen's κ score (score 0. 74 based on disagreement of 268/2268 points). Extraction of data from studies was carried out in Microsoft Excel (2010, Microsoft Corporation, Redmond, Washington, USA) using a prepiloted form that was tested on two randomly selected studies from 2010. For each report, we also extracted the following data: the number of authors, the continent where the study was conducted, multicentre status, number of study participants and reporting of ethics review and conflict of interest. For each journal included, we obtained the ISI 2011 impact factor and whether or not the journal endorsed (eg, recommended or required) the CONSORT statement and CONSORT-NPT extension (information obtained in 2012).
As previously described, an interrupted before-after study found only moderate improvement between 2004 and 2010. 17 Our author survey suggested that there had been an increase in the proportion of authors who were aware of the existence of the CONSORT-NPT extension between the time of submission and now. Whether this translates to improved adherence at the current time is unknown. A third limitation includes the fact that we did not assess study protocols for adherence to CONSORT criteria. Some authors may have included additional study details within the protocol. A final limitation concerns the author survey. We were restricted in our ability to analyse these data by the poor response rate and the significant time lag between submission of the RCTs (c. 2010) and distribution of the survey to the corresponding authors in 2013.
Acknowledgements GK was supported by  NSF   CISE grants   0524202  and  0915928  by a  Cisco Systems URP  gift; PA was supported by  EINS , the  Network of Excellence in Internet Science EC 's  FP7  grant  288021. YJ was supported by  NRF-2013R1A1A3A04007104. Elements of this paper were presented at the  IEEE CDC, Atlanta , 2010 and AdHocNets, Paris, 2012 conferences.
This fails to be relevant when historical data do not represent the actual evolution of indicators and, especially, with climate risk indicators, in the context of climate change. Thus, it is important to introduce new dynamic tools, as dynamic risk measures, that allow dynamic comparisons when parameters evolve. Although the risk measure developed in the present article can be used for several risk indicators, we have chosen to focus on heat wave risk. Heat waves lead to losses for insurance companies, due to the excess of mortality, business interruption or other phenomena. One of the main conclusions of the 5th IPCC report is the transient climate response, which quantifies the response of the climate system to an increasing radiative forcing on a decadal to centennial timescale. It is defined as the change in global mean surface temperature at the time when the atmospheric carbon dioxide concentration has doubled in the scenario of the concentration increasing by 1% per year.
Acknowledgments: SYRTE is UMR CNRS 8630. We acknowledge funding by  LNE ,  CNES ,  IFRAF ,  DGA ,  EMRP  project  T1 J2. 1   OCS ,  ESA  project  SOC1 ,  FP7  project  SOC2. We thank  Christophe Salomon  and  Thomas Udem  for fruitful discussions on the manuscript.
Design : A cross-sectional review. Sample: We assessed 200 systematic reviews of randomised trials published between January and March 2012; Cochrane (n=100), non-Cochrane (Database of Reviews of Effects) (n=100). Main outcomes: Our primary outcome was a descriptive analysis of how assessments of risk of bias are carried out, the methods used, and the extent to which such assessments were incorporated into the statistical analysis and overall review findings. Results: While Cochrane reviews routinely reported the method of risk of bias assessment and presented their results either in text or table format, 20% of non-Cochrane reviews failed to report the method used and 39% did not present the assessment results. Where it was possible to evaluate the individual results of the risk of bias assessment (n=154), 75% (n=116/154) of reviews had ≥1 trial at high risk of bias; the median proportion of trials per review at high risk of bias was 50% (IQR 31% to 89%).
Strengths and limitations of this study ▪ Our sample of non-Cochrane reviews was drawn from the Database of Reviews of Effects, which meets strict methodological criteria. It is possible that our findings might be an underestimate of the problem compared to systematic reviews identified from other sources. assignment status are more likely to report symptoms, leading to biased results. 2 3  In addition, selective reporting means that significant trial outcomes are more likely to be reported than those with non-significant outcomes. 4 An assessment of the validity of individual studies included in a systematic review, and the risk that they might overestimate or underestimate the true intervention effect, is therefore a critical part of the systematic review process. The assessment of the risk of bias of studies included in a systematic review has evolved over time.
Systematic review selection and inclusion criteria We assessed a convenience sample of 200 systematic reviews that evaluated randomised trials assessing the effects of healthcare interventions published between January and March 2012. We sampled systematic reviews from two specialised databases: those published in the Cochrane Database of Systematic Reviews (n=100) in the Cochrane Library (http://www. cochranelibrary. org) and those from the Database of Reviews of Effects (DARE) (n=100) through the Centre for Reviews and Dissemination, University of York (http://www. crd. york. ac. uk/crdweb). Systematic reviews included in DARE must meet strict methodological criteria, and thus we deemed them to be of a similar methodological standard to Cochrane systematic reviews. We excluded updates of previously published systematic reviews and those published in languages other than English.
Data extraction Data extraction was carried out by teams of assessors working in pairs, and any uncertainties or disagreements were resolved by involving a third assessor. Systematic reviews were allocated at random such that each assessor extracted a similar number of Cochrane and non-Cochrane systematic reviews. Prior to startingdata extraction, the assessors received training on how to complete the data extraction form (see online supplementary appendix 1). For each systematic review, we recorded the systematic review type (ie, Cochrane or non-Cochrane), medical specialty, type of intervention(s) and the number of included randomised trials. We assessed the method used to assess risk of bias of the included trials (ie, whether they used a summary scale, checklist or assessment of individual methodological components), the type of tool used (eg, the Cochrane Risk of Bias tool, the Jadad scale, the Pedro scale, etc), how the risk of bias assessment was carried out, by whom and which individual methodological components were assessed.
Searches of the Cochrane Database of Systematic Reviews and the DARE between 1 January and 31 March 2012 identified 281 reports of systematic reviews. We assessed the full texts of all articles to confirm eligibility and that they were systematic reviews of randomised trials. We excluded 44 non-Cochrane reviews and 23 Cochrane reviews (see figure  1  for reasons for exclusion). After exclusions, we selected at random 100 non-Cochrane and all remaining 95 Cochrane reviews (five additional Cochrane reviews were selected at random from the April 2012 issue of the Cochrane Database of Systematic Reviews to increase this sample to 100). The most common medical specialties of the included reviews were cardiology (n=20/200; 10%), neurology (n=19/200; 9. 5%), obstetrics and gynaecology (n=19/200; 9.
Methodological components assessed Overall, the median number of individual methodological components assessed per systematic review was six (IQR 5 to 7), ranging from 1 to 27 items (table  2 ). Nearly all Cochrane reviews assessed the method of random sequence generation (100%), concealment of the allocation sequence once randomised (100%), blinding (99%) and incomplete outcome data (ie, missing outcome data due to attrition) (95%) compared to 62%, 60%, 69% and 61% of non-Cochrane reviews, respectively. Very few systematic reviews (Cochrane: 7%; non-Cochrane: 2%) assessed blinding separately for more than one outcome measure or incomplete outcome data for more than one outcome (eg, where the outcome was measured at different time points) (Cochrane: 8%; non-Cochrane: 1%). Evidence of selective outcome reporting was assessed in 86% of Cochrane reviews compared to only 20% of non-Cochrane reviews.
Acknowledgements The authors are very grateful to  Florence Aim ,  Ali Alkhafaji ,  Soraya Belgherbi ,  Celine Buffel du Vaure ,  Thierry Bultez ,  Solene Delpy ,  Julie Fort ,  Guillaume Lonjon ,  Daniela Louis ,  Valeria Martinel ,  Ahmed Nizar ,  Cecile Pino ,  Coralie Poulton ,  Valerie Seegers  and  Claire Thuillier  for their assistance with data extraction, and to  Agnes Dechartres  for her assistance with coordination of the data extraction activities.
Background Health-care systems face a crisis of an increasing burden of chronic diseases aggravated by aging populations  [1]. It is of much importance that policy makers and healthcare managers can make decisions based on sufficient knowledge and understanding of chronic care activities. This is especially true in the field of cancer where incidence, therapeutics, practices and costs can vary quickly  [2, 3]. On the one hand, policy-makers need cost-effectiveness and cost-of-illness analyzes for planning and fair allocation of funding. On the other hand, care providers should be able to adapt their resources and costs while they share patients in multidisciplinary and coordinated approaches. Costs can be estimated from a variety of data sources, including insurance claims, billing systems, hospital discharge databases and surveys  [4].
However, data sources may vary in a number of important aspects: accessibility, representativeness, level of aggregation, period of observation, availability and accuracy of clinical data. Besides, discrepancies can be observed depending on the source used to identify cases or estimate medical expenditures  [5]. In parallel to ad hoc surveys that are often temporary and costly, administrative data are routinely collected in perennial information systems. They are an easily accessible source of information to analyze the economical burden of chronic diseases  [6]. Moreover, when they contain enough clinical details, claim databases have proven to be useful in the field of epidemiology  [7] [8] [9] [10] [11] [12]. Though essentially used for funding and analysis of isolated episodes of care, this combination of medical and economical information may contain sufficient ingredients to study trajectories of care, giving better and more comprehensive insights on the journey of chronic patients in the healthcare system  [13].
In France, the Programme de Médicalisation des Systèmes d'Information (PMSI) is a nationwide information system, derived from the Diagnosis Related Groups (DRG) system  [14]. Initially build for billing purposes, the PMSI system has two important advantages for the analysis of trajectories of care. Each sector of activity (acute, post-acute, psychiatric care) is covered by an information system common to the whole French population. Second, since the introduction of an anonymised identifier in 2001, it allows the linkage of all hospitalizations of a same patient across time, space and sectors of activity. However, as most of existing patient classification systems, the PMSI focuses on single contacts and was not designed to categorize a care process spanning several encounters. For chronic conditions, it is of much interest to summarize the information contained in a set a longitudinal data and produce meaningful categories that will be relevant for subsequent analysis.
This is a difficult and time consuming classification task, as chronic patients can have multiple diagnoses and multiple treatments recorded in several different facilities, and because it is an indirect and a posteriori use of data that were initially collected for budgetary purposes. Besides, different classifications may be required to achieve different goals. Meanwhile, data mining methods may support the experts in the categorization and analysis of trajectories of care  [15]. In this article, we propose a method for grouping trajectories of care over a sequence of hospitalizations, using claim data. Our approach relies upon Formal Concept Analysis (FCA), a conceptual clustering method, and data from the PMSI. We studied one-year trajectories of care of the patients having undergone breast cancer surgery in 2009 in France.
The PMSI database The PMSI is the French casemix system database. It is mandatory in all public and private hospitals where each admission triggers the collection of a minimal set of data holding administrative and clinical information. The PMSI database hold 24,575,239 stays in 2009. The coding of diagnoses and procedures forms the basis of information needed for the definition of patient groups. Diagnoses are coded with the 10 th International Classification of Diseases (ICD-10) and medical procedures with the french nomenclature "Classification Commune des Actes Médicaux (CCAM)". In the PMSI, diagnoses can have different roles: principal, related or comorbidity. The principal diagnosis is the condition problem that is chiefly responsible for occasioning the admission of the patient to the hospital for care.
When it is chosen in Chapter XXI (Factors influencing health status and contact with health services), the principal diagnosis may be precised by a related diagnosis giving the aetiology. Table  2  gives an example of an inpatient record in the PMSI database. A national scale of costs per DRG for hospitals is computed each year, by measuring costs of stays in about 50 hospitals. Since 2001, an anonymised patient identifier makes it possible to link all the stays of a given patient in different hospitals.
Building the trajectory of care We used data from the National PMSI database including all hospitalizations in public or private hospitals for years 2008 to 2010. Data were obtained after approval from the CNIL, the so-called French Data Protection Authority. Data processing steps are described on Figure  2. For the first step (1), all stays for surgical breast cancer treatment in 2009 were identified. A stay was selected by the co-occurrence of an ICD-10 breast cancer code (C50* or D05*) and a CCAM code of breast surgery. For a given patient, the first identified stay in 2009 was considered as the index stay (setp 2). We looked for previous similar situation in 2008 to check that this index stay was indeed a as far as possible a "new case". Step 3 consisted in building the one year care trajectory for each patient identified  at step 2.
In our study, FCA has produced clinically meaningful categories based on sets of diagnoses. These categories are the result of frequent co-occurrences of ICD 10 codes in the patients care trajectories. In this, FCA can be considered as a "white box" model with a straightforward interpretation. We represented diagnoses by their 3-digit ICD codes. A finer grained representation would have resulted in over specific and small categories, sensible to instability of coding. The level of granularity chosen for the attributes is to be taken in consideration to achieve a compromise between generality and specificity of the concepts. As an interesting side effect, FCA can also be used to assess quality of data in the DRG system. We discovered that 1017 patients had both D05 (in situ) and C50 (invasive) breast cancer codes.
A closer look at the data revealed that the main diagnosis at inclusion was C50 for 449 (44%) of them, meaning that D05 was recorded subsequently. It is likely that there are coding errors, though we have no evidence at this time to distinguish them from multiple tumors. The concept (C50, D05) could also reflect genuine diagnosis errors. Besides the potential clinical consequences of such errors, our work suggests that they have an economical impact: the cost of concept (C50, D05, Z511) shows a difference of 1,926e compared to concept (C50, Z511). This kind of fortuitous discovery is actually one of the goals and one of the benefits of data driven approaches. We analyzed care trajectories in the field of breast cancer. Since 2001, the French casemix information system uses an anonymized identifier linking all the contacts of a given patient with the healthcare system.
It is now possible to study groups of patients at a national scale and to analyze resource use in an quasi-exhaustive manner. Thus, our results are not submitted to sampling error. However, they strongly depend on the algorithm used to select patients and build the care trajectory. Several studies have assessed the use of administrative databases to detect cancer cases  [12, 36, 37]. Our method selects patients with surgical breast cancer on a combination of diagnosis and procedure codes which can reduce the false positive rate  [38]. The constitution of the care trajectory should be elaborated in order to minimize censoring bias. We standardized the observation window by fixing the start of the trajectory as the first stay for breast cancer surgery and recording all the stays occuring within one year, a sufficient follow-up time to capture the first phase of treatment.
Our method brings also valuable information for hospitals taking part in repetitive treatments (for example annual costs of care per patient, number and total length of episodes of care). Such information is generally unknown from hospital managers because hospital information systems are not aware of what is happening outside the facility in a trajectory of care. At a regional level, this information may help hospital managers to better plan recruitment of patients according to local needs and other hospitals activity. Moreover, as the French system is incited by authorities to develop multidisciplinarity and collaborations, the analysis of care trajectories is essential for implementing more integrated care processes. This work shows how administrative databases and data mining methods can be used to produce descriptions of care trajectories that are both medical and economical.
Indeed, fee-for-service may introduce imbalance funding between the different parts of some trajectories. A global vision of the trajectory can be necessary to redistribute and equilibrate resources between the healthcare providers through prices adjustment, while keeping the overall care expenditure constant. Our approach is limited by the availability of linkable data. In France, an anonymized identifier can track a patient along its journey through the healthcare system at a national scale. Even though health administrative databases are implemented in many countries, such an identifier may not always exist or may have a limited coverage of the population. Our study focuses on breast cancer but other chronic pathologies can be considered. However, for many of them, care is delivered on an ambulatory basis.
Response to the survey We surveyed 1,503 corresponding authors of recently published RCTs indexed in PubMed, and 1,777 peer reviewers for NEJM (n = 227), JAMA (n = 773), and ARD (n = 777). The flow of participants is in Figure  S1. Of the 3,033 participants invited to complete the survey, 1,165 responded and 1,136 respondents submitted useable data (37. 5%): 713 from authors and 423 from reviewers.
Alternatively, as several of our respondents suggested, managing or academic editors could themselves verify that the trial had been registered before patient recruitment and could check that there are no discrepancies between the registered and published items (primary outcome, inclusion and exclusion criteria, sample size, time of assessment, etc. ) before sending the manuscript to peer reviewers. One-quarter of respondents who did not examine the registry record when they peer reviewed a manuscript stated that the reason was a perceived lack of usefulness of registered information. Previous studies have found that the completeness and quality of information in registries is variable. Zarin et al. assessed  ClinicalTrials. gov data that were publicly available for a sample of 3,284 registered trials.
Of the 2,324 posted results entries that had been posted publicly, 14% were linked to a PubMed citation through an indexed ClinicalTrials. gov registration number. Of 2,178 clinical trials with posted results records, 20% had more than two reported primary outcomes measures and 5% more than five. For some studies, posted results include more than 100 primary and secondary outcome measures  [16]. The medical community should emphasize the importance of trial registration and ensure that proper information is submitted to registries. There may be legitimate discrepancies between the manuscript and registry record, but these should be transparently reported in the paper or in the registry so that readers can judge the potential for bias. When incomplete or uninformative information is provided in registry records, journals could consider these trials inadequately registered and could ask authors to complete this information or explain why the study is not correctly registered.
In the main analysis, the design parameters of QLCRM were as follows: we obtain the vector of initial guesses, also called working model, with the getprior function of the R-package dfcrm developed by Cheung  [18] , using dose 3 as the prior guess of RD, 0. 04 as the indifference interval parameter, and a logistic model with the intercept set at 3. Please refer to the paper of Lee  [19]  for more details. For the QCRM, we similarly generated the working model from the getprior function (the prior guess of RD equal to dose 3 and the indifference interval parameter equal to 0. 04 and the empiric model). The prior distribution of the empiric model parameter b is the exponential distribution with mean 1, as proposed by the authors  [6]. We studied some other parameters that will be discussed later.