dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensitivity analyses were carried out by additionally adjusting models for the overall quality of the diet, using the sPNNS-GS2 score. Interactions were tested between each nutritional factor and sex by introducing the product of the corresponding 2 variables in the model. We also tested restricting our study sample to a nested case-control design, with matching for age, sex, and residential area (4 controls per case). All tests were two-sided and P< 0. 05 was considered statistically significant. Analyses were carried out using SAS 9. 4 (SAS Institute Inc. , USA).
Acknowledgements The authors warmly thank all the volunteers of the NutriNet-Santé cohort for their continuous participation in the study and for participating in the  SAPRIS-SERO  project. The authors also thank  Cédric Agaesse  (manager),  Alexandre De-Sa  and  Rebecca Lutchia  (dietitians);  Thi Hong Van Duong ,  Régis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Nicolas Dechamp  (data-manager/statisticians);  Sandrine Kamdem  (health event validator); and  Maria Gomes  (participants' support) for their technical contribution to the  NutriNet-Santé study , for their dedication and engagement to collect and manage the data used for this study, and for ensuring continuing communication with the cohort participants. We thank the  CEPH-Biobank  staff for their adaptability and the quality of their work.
Funding The  SAPRIS/SAPRIS-SERO  projects received funding from  ANR  ( Agence Nationale de la Recherche , # ANR-20-COVI-000 , # ANR-10-COHO-06 ),  Fondation pour la Recherche Médicale  (# 20RR052-00 ), and  Inserm  ( Institut National de la Santé et de la Recherche Médicale , # C20-26 ). The  NutriNet-Santé cohort study  was supported by the following public institutions:  Ministère de la Santé ,  Santé Publique France ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Université Sorbonne Paris Nord. Study investigators are independent from the funders. Funders had no role in the study design, the collection, analysis, and interpretation of data, the writing of the manuscript, or the decision to submit the article for publication.
Availability of data and materials Researchers from public institutions can submit a collaboration request including their institution and a brief description of the project to collaboration@etude-nutrinet-sante. All requests will be reviewed by the steering committee of the NutriNet-Santé study. A financial contribution may be requested. If the collaboration is accepted, a data access agreement will be necessary and appropriate authorizations from the competent administrative authorities may be needed. In accordance with existing regulations, no personal data will be accessible.
Study Design and Settings This observational, retrospective cohort study was conducted from January 2006 to December 2019 in the Medical ICU of Montpellier University Hospital. In our hospital, this unit is the chief site of onco-hematology and kidney transplant patient management whenever they present a severe and life-threatening condition necessitating mechanical ventilation or/and hemodynamic support. The study was registered at ClinicalTrials. gov (No. NCT04647539) on 23 November 2020.
Patients During the study period, eligible patients were screened through the local mycology laboratory database and the hospital coding records. Medical charts of patients admitted to the ICU with a positive culture for molds other than Candida and Aspergillus were independently reviewed by two ICU physicians (R. L. and L. P. ) and a mycologist (Nathalie Bourgeois) to confirm and secure an EIFI diagnosis according to European Organization for Research and Treatment of Cancer (EORTC) and European Confederation of Medical Mycology (ECMM) criteria  [8, 9]. A proven EIFI was retained in a host with illness consistent with EIFI and mycological evidence of EIFI or a pathognomonic histopathologic or direct microscopic exam of EIFI from blood or a specimen obtained from the affected site. A probable EIFI was retained in the presence of a host factor plus a clinical picture consistent with EIFI and mycological evidence from urine, blood or CSF.
Survival at ICU discharge and day 90 and date of death, if applicable, were recorded. We then identified predictive factors of in-ICU mortality. We compared clinical data and the prognosis of this cohort to that of a control group of patients with aspergillosis in order to best evaluate the impact of EIFIs in the critically ill. Controls were selected among patients admitted to our ICU during the same period with a diagnosis of aspergillosis according to Infectious Diseases Society of America (IDSA) guidelines  [21]. Controls were then matched based on age, SOFA score, development of ARDS and previous conditions (at least 3 similar conditions among hemopathy/cancer, bone marrow/solid organ transplantation, diabetes, chronic kidney disease and malnutrition).
Characteristics of the Study Population During the study period, a total of 26 cases were identified and confirmed from our database. Given that 6900 patients were admitted in our ICU concomitantly, incidence of EIFI was at 4 per 1000 admissions (Figure  1 ). Characteristics of the study population are summarized in Table  1. Patients were mainly male (81%) with a median age of 59 (48-69) years old. All of them had one or more predisposing factors for EIFI, including drug-induced immunodepression (85%, induced by corticosteroids or immunosuppressive drugs), hematological malignancies, transplantation and malnutrition (81%). A majority of patients were previously treated with broad- Characteristics of the study population are summarized in Table  1. Patients were mainly male (81%) with a median age of 59 (48-69) years old.
This A713G mutation was not identified in 103 ORF11 sequences obtained in GenBank from strains isolated in the United Kingdom, United States, China, Australia, Belgium, New Zealand, Japan, or India. Furthermore, this mutation was not identified in 131 ORF11 sequences from strains isolated in Ireland or in 15 ORF11 sequences from EHV-1 strains isolated in France. Although we cannot exclude the existence of this mutation in other strains from the field, its absence in the 249 ORF11 sequences analyzed suggests that this SNP constitutes a possible marker to identify horses infected with the virus strain linked to the Valencia outbreak. Because the Federation Equestre Internationale (Lausanne, Switzerland), the international governing body of equestrian sports, cancelled international events in 11 countries in Europe during March 1-April 11, 2021, the detection of this SNP might be helpful for investigating the extent of virus spread in different countries.
Data analysis Each recording was transcribed verbatim and the whole corpus of the interviews was then prepared for textual analysis. The data from the interviews were processed from November 2018 to March 2019. Thematic content analysis was performed with Nvivo software on each transcription until thematic saturation was reached, that is when no new themes emerged in the last five transcriptions analysed. While thematic content analysis allows to interpret the corpus of texts, we also performed lexicographical analysis to get a better understanding of what our participants talked about  [37, 38]. This approach allows observation of the frequency and the proximity or distance between words, by drawing up a lexicon of the words used in the corpus, and dividing them into units which will be the subject of a matrix (presence/absence of words in each unit).
Study design We conducted a randomized single-center open-label superiority trial (EudraCT/ID-RCB 2012-A00078-35) in parallel groups, from June 2012 to February 2019 in the medical and surgical intensive care units of the University Hospital of Besancon (Besancon, France). The study protocol was approved by the Institutional Review Board (CPP Est-II, University Hospital of Besancon no. 12/639), and by the French National Health Products Safety Agency (ANSM, Saint-Denis, France no. 2012-A00078-35). The study was conducted in accordance with the French legislation on bioethics  [16]. The results are reported in compliance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines.
Data and endpoint measures Demographic data, past medical history, Simplified Acute Physiology Score (SAPS) II score and Glasgow Coma Score (GCS) were recorded at admission to the ICU. Gastric residual volume was measured every 12 h. The following data were collected every day during the study period: body weight, sedation (yes/no), catecholamine infusion (yes/no) and the total volume of EN administered. The daily ratio of volume of EN administered to the volume prescribed according to the protocol was calculated. Blood levels of liver enzymes, albumin and prealbumin were measured at inclusion, at 5 and 10 days after inclusion. The primary endpoint was the percentage of patients who received both 60% of the daily energy intake goal 3 days after inclusion and 100% of the daily energy intake goal 5 days after inclusion.
Nutritional impact was evaluated by the daily energy and protein intakes delivered and on the variation of blood albumin and prealbumin measured at baseline, at Day 5 and at Day 10 after inclusion. Morbidity events considered at 28 days were duration of invasive mechanical ventilation, the length of stay in the ICU and onset of pneumonia. Pneumonia was defined according to the French guidelines by the presence of: fever > 38. 3 °C without any other cause, purulent sputum or tracheal aspiration, declining oxygenation or increased oxygen-requirement and new or progressive lung infiltrates on chest radiographs  [19]. The reliability of all data collected was assessed by an independent data manager at the end of the study.
Intergroup comparisons were performed using the Chi-square or Fisher's exact test for qualitative variables and the Student t or Mann-Whitney U test for quantitative variables, as appropriate. Repeated measurements of daily energy and protein intakes, blood levels of albumin and prealbumin were compared between groups using repeated measure ANOVA. The analysis was neither adjusted nor stratified for additional variables. No subgroup analysis was performed. All statistical analyses were performed with SAS software, version 9. 4 (SAS Institute Inc. , USA), and the significance level was fixed at 0. 05.
Funding The work was not funded by  National Institutes of Health (NIH) ,  Howard Hughes Medical Institute (HHMI), Medical Research Council (MRC) , and/or  Wellcome Trust. The work was not supported by official funding or a grant. The financial cost of the study was supported by the  Department of Anesthesiology and Intensive Care Medicine , 3  Bvd Alexander Fleming ,  University Hospital of Besancon, F-25000 Besancon, France.
Systematic review A systematic review of randomised controlled clinical trials was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,  28  with the aim of studying the efficacy of TNFi according to different demographic and disease-related factors. The subgroups of interest were selected according to smoking status, physical activity, sex, age, body mass index (BMI), autoantibody status, disease duration and disease activity score at baseline. The protocol was registered in the PROSPERO database (number CRD42018071079) in January 2018 and was updated in July 2021. We searched for randomised controlled trials (RCTs) and meta-analyses of RCT in Cochrane Central Register of Controlled Trials. This research was performed until January 2017 using the keywords "rheumatoid arthritis", "infliximab", "adalimumab", '"etanercept", "golimumab" and "certolizumab".
Two authors (TB-A and SD) selected eligible RCT on title and abstract, retrieved full text of eligible articles and decided on the final inclusion. We included RCT comparing TNFi to placebo or csDMARDS in patients with RA, with no restriction on the presence or not of previous TNFi. We excluded non-randomised studies and studies comparing two TNFi without a placebo or csDMARD control groups. If included studies did not report efficacy of TNFi according to subgroups of interest, we sought to obtain individual patient data (IPD).
Data collection Given the unavailability of published subgroup of interest analyses, we contacted the corresponding authors and/ or sponsors of these trials in order to obtain aggregated data and/or IPD and to perform a pooled analysis. Since most of the data were stored securely on data-sharing platforms, we requested each of these platforms an access to the raw dataset. We signed data use agreements for two platforms, Yale Open Data Access (YODA) and Vivli Centre for Global Clinical Research, that allowed us to access IPD for 26 trials. We further obtained IPD for two AMGEN trials and for one academic trial (online supplemental table  1 ).
Outcomes The predefined primary endpoint was ACR20 score after 6 months of follow-up, and secondary endpoints were ACR50, ACR70, DAS28(CRP) and DAS28(ESR). Due to the impossibility to obtain ACR response from the raw dataset for eight trials, we decided in November 2019 to modify the primary endpoint to between-treatment group differences in DAS28(CRP) change (ΔDAS28(CRP)) from baseline to 6 months (or as close as 6 months, depending on each trial available data). Secondary endpoints were between-treatment group differences in final DAS28(CRP) measured at 6 months (or as close as 6 months) and EULAR response criteria at 6 months (or as close as 6 months). 29 EULAR response criteria were used to stratify the groups of response, that is, good response if final DAS28 was ≤3. 2 with a DAS28 improvement from baseline of at least 1.
Statistical analyses Statistical analyses were performed using R Studio software. Descriptive results are presented as median (minmax) or mean (IQR) unless stated otherwise. Pooled ORs or mean difference with 95% CIs for EULAR response and for DAS28(CRP) differences, respectively, between TNFi and placebo were calculated using two-step metaanalyses. First, aggregate data regarding treatment response in each subgroup of interest were estimated from IPD. Second, a random-effect Mantel-Haenszel model was applied to calculate pooled effect. We considered a significant difference between subgroups if the p value is <0. 05. Between-study heterogeneity was quantified using Cochrane Q and I² statistics. Heterogeneity was considered low if I 2 was <30%, moderate if I 2 was 30%-50%, substantial if I 2 was 50%-70% and considerable if I 2 was >70%.
Search process We found 496 articles published between 1994 and 2017, and 220 of them were eligible after selection on title and abstract, and 79 fulfilled all inclusion and exclusion criteria (figure  1 ). At the end of our search, we did not retrieve any RCT that reported aggregated results regarding the efficacy of TNFi in the subgroups of interest. We therefore retrieved IPD for 29 trials. As to June 2021, we obtained access data for 8 trials from the YODA platform, 18 trials from the Vivli platform, 2 trials from AMGEN and 1 trial from an academic author (figure  1 ).
Metaregression analyses Bivariable and multivariable metaregression results are shown in table 4. Metaregressions were performed on 7457 patients (18 RCTs) from Vivli and on 3767 (8 RCTs) patients from YODA. Bivariate analyses indicated that being treated by TNFi was associated with a significantly lower final DAS28(CRP), while a higher baseline DAS28(CRP) was associated with a significantly higher final DAS28(CRP) in trials from both databases. Men presented a lower final DAS28(CRP) than women, and disease duration was associated with an increased final DAS28(CRP) in trials from the Vivli database. Significant treatment effect modifiers in bivariate analyses were disease duration with a lower 0. 02/year (in trials from the Vivli database) and baseline DAS28(CRP) with a higher 0. 1/baseline unit (in trials from YODA database) final DAS28(CRP) in treated patients.
Four variables, that is, age, sex, BMI and disease duration, were included in the multivariable models. Using the individual data from Vivli (n=7457), the model indicated that disease duration as a continuous variable and baseline DAS28(CRP) as a categorial variable significantly modified treatment effect on final DAS28(CRP). Patients treated by TNFi had a final DAS28(CRP) decreased by 0. 02 for each year of disease duration (p<0. 001), and a 0. 21 decreased for patients with a baseline DAS28(CRP) >5. 1 (p=0. 05). These results were not observed in the metagression performed on the 3767 patients from the YODA platform.
The use of IPD from data-sharing platforms enables reusing raw data from a substantial number of studies. Furthermore, in comparison with meta-analyses based on aggregated data published by the investigators, obtaining raw data allowed us to study in a standardised manner various parameters and to pool them together, creating a large database. The results obtained here have important clinical impacts in the context of personalised treatment strategy, for example, to increase awareness on negative predictive factors such as obesity when initiating a TNFi. The main challenge in our work was the data selection and availibility. In most cases, we chose to exclude from the subgroup analysis patients who could not be categorised, which may have caused bias in the analysis. 35 Some adjustments were nevertheless made to limit this loss of data.
Rheumatoid arthritis Rheumatoid arthritis Rheumatoid arthritis data contributors AbbVie, Pfizer and UCB that have been made available through Vivli. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication. We thank Professor Laure Gossec and Professor Cécile Gaujoux-Viala for offering their advice. Contributors DM coordinated the work, participated in the interpretation of the analyses and helped draft the manuscript. TB-A initiated the project, performed the statistical analyses and helped draft the manuscript. SD wrote the protocol of the meta-analysis, selected the studies and asked for data. M-AS and JL-W ensured obtainment of data, analysed the results and drafted the manuscript. NA handled the database and helped in improving the manuscript.
7 74. 8 74. 4 58. 5 89. 8 79. 1 83. 5 76. 8 80. 1 80. 7 70. 1 92. 2 75. 9 80. 7 84. 2 78. 6 82. 5 76. 9 84. 6 82. 1 78. 8 82 81. 5 80. 8 78. 6 71. 2 Physical activity (%) 13. 6 18. 9 25. 1 23. 5 28. 2 Smoker (%) 49. 8 48. 9 13. 6 20. 9 13. 3 57. 1 36 46. 3 32. 3 38. 9 51 34. 9 9. 4 Analysed Studies TNFi patients NCT00647491 Adalimumab 312 NCT00420927 Adalimumab 857 NCT00195663 Adalimumab 630 NCT00195702 Adalimumab 492 NCT00234845 Adalimumab 94 NCT00235859 Adalimumab 118 NCT00647920 Adalimumab 43 NCT00538902 Adalimumab 278 NCT00647270 Adalimumab 336 DE31 Adalimumab 574 NCT00870467 Adalimumab 264 PMID22739990 Adalimumab 134 PMID9920948 Etanercept 64 NCT01313208 Etanercept 195 NCT00445770 Etanercept 462 NCT00160602 Certolizumab 368 NCT00152386 Certolizumab 587 NCT00717236 Certolizumab 936 NCT00548834 Certolizumab 103 NCT00674362 Certolizumab 143 NCT01519791 Certolizumab 860 566 NCT00264537 Golimumab 396 NCT00264550 Golimumab 335 NCT00299546 Golimumab 567 NCT00361335 Golimumab 546 NCT00973479 Golimumab 234 NCT01248780 Golimumab 276 NCT00269867 Infliximab 847 NCT00236028 Infliximab Data are expressed as means.
*Exact data expressed in intervals. ACPA, anticitrullinated protein antibody; BMI, body mass index; CRP, C reactive protein; DAS28, Disease Activity Score on 28 joints; PGA, Patient Global Assessment; RF, rheumatoid factor; SJC28, swollen joint count; TJC28, tender joint count; TNFi, tumour necrosis factor inhibitors. on April 15, 2022 by guest. Protected by copyright. http://rmdopen. bmj. com/ RMD Open: first published as 10. 1136/rmdopen-2021-001882 on 17 November 2021. Downloaded from Law-Wan J, et al. RMD Open 2021;7:e001882. doi:10. 1136/rmdopen-2021-001882.
Background: Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM).
We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. Results: PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate.
Introduction Among the different omics fields, metabolomics is the most recent and provides new insights for a global study of biological systems. Metabolomics is a rapidly growing and promising field of research in biology and healthcare. Metabolomics approaches are based on the determination of the levels of different small molecules or metabolites in biological samples (tissue, cells, serum, urine. ). Interestingly, ever since the early metabolomics studies, supervised classification methods have been used for the analysis of the related datasets. One of the initial aims of metabolomic studies was to establish useful biomarkers, indicative of specific physiological states or aberrations. The challenge now is to understand the mechanisms by which changes in the metabolome are implicated in different phenotypic outcomes in a complex systems biology approach  [1, 2].
Most metabolomics studies generate complex multivariate datasets including varying correlations between features and systematic noise. Therefore, multivariate data analysis methods are needed to explore these datasets. One of the most frequently used methods for metabolomics analyses is Partial Least Squares-Discriminant Analysis (PLS-DA)  [3, 4]. PLS-DA is a chemometric technique used to optimize separation between different classes of samples, which is accomplished by linking two data matrices: X (raw metabolomic data) and Y (class membership). It has the advantage of handling highly collinear and noisy data. Yet, it has some drawbacks and needs to be handled with caution. Indeed it has been reported that PLS-DA can: 1. Lead to over-fitting when the number of variables significantly exceeds the number of samples.
Yet, Random Forests have significant drawbacks. For instance, they tend to over-fit when using noisy datasets. Furthermore, the main disadvantage of Random Forests is their complexity. Indeed, they are much harder and time-consuming to construct, require more computational resources and are less intuitive than decision trees. Furthermore this complexity significantly hampers their interpretability. Support Vector Machines (SVM) are another option  [11, 12]  but have similar drawbacks as Random Forests and are particularly consuming in computational resources. Mathematics I3S partner has recently introduced a new tailored, constrained primal-dual method for supervised classification and feature selection  [13]. This method has the significant advantage of providing a trustworthy confidence index with each prediction, which we use to define a new classifier with rejection.