dataesr/scientific-paragraphs-categorization huggingface dataset:
ITR was funded by the  Italian Ministry of Health. MUBICOs has been partly funded by an unrestricted grant by the  Chiesi Foundation (Parma, Italy). The Turin study was made possible by a grant of the  University of Turin  to  Roberto Bono  (ex 60% 2013) and a grant of  Regione Piemonte  to  Pavilio Piccioni  (Ricerca finalizzata 2011). The Viadana study was funded by the local  National Institutes of Health  ( Asl Mantova ) and by the province of Mantua. The Fumane study was funded by the municipality of Fumane. EDEN was supported by the  Foundation for Medical Research (FRM) ,  National Agency for Research (ANR) ,  National Institute for Research in Public Health (IRESP: TGIR cohorte santé 2008 program ),  French Ministry of Health (DGS) ,  French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A)  and  Human Nutrition National Research Programs, Paris-Sud University, Nestlé ,  French National Institute for Population Health Surveillance (InVS) ,  French National Institute for Health Education (INPES) , the  European Union   FP7  programmes (FP7/2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects),  Diabetes National Research Program  (through a collaboration with the  French Association of Diabetic Patients (AFD)) ,  French Agency for Environmental Health Safety (now ANSES) ,  Mutuelle Générale de l'Education Nationale (MGEN) ,  French National Agency for Food Security , and the French-speaking association for the study of diabetes and metabolism (ALFEDIAM).
HISs have been developed in the last decades mainly to support and improve healthcare processes, decisions, and outcomes of patients. Nowadays HISs are ubiquitous in hospitals and it is difficult to find a hospital without an information system. One can distinguish, among HISs, different kinds of systems dedicated to healthcare support. According to  Shortliffe and Cimino (2014) 's review of computer applications in healthcare, one of the first systems developed in healthcare environments corresponded to systems allowing the recording of healthcare information. These are Electronic Health Records (EHRs), including databases, indexing systems, and research systems using healthcare information. With a similar objective, Computer Physician Order Entry (CPOE)  (Kuperman and Gibson, 2003) , are systems developed to digitize physician's orders.
, 1992). Guideline-based DDSSs encompass "expert systems", which integrate "gold-standard" flowcharts/rules into their process to produce full-fledged diagnosis recommendations to physicians  (Yanase and Triantaphyllou, 2019) , but also systems that prescribe to physicians the steps they should follow to abide by the "gold-standard" (this is the case, for example, of the systems found on the NICE website or the Quick Medical Reference (QMR) linked to the INTERNIST expert system  (Miller et al. , 1986; Miller, 2010) ). -DDSSs based on Machine Learning (ML) algorithms, or ML-based DDSSs, are used to support diagnoses of specific diseases, with the aim to minimize error rates by treating large amounts of data on patients  (Dua et al. , 2014; Yanase and Triantaphyllou, 2019). ML algorithms are methods used to learn how to approximate a classification function based on a learning dataset.
Classification functions could be, for example, functions anticipating the value of an exogenous variable depending of the value of an endogenous variable , or functions distinguishing pictures of healthy from pictures of diseased organs by analyzing a matrix of pixels. ML problems are generally divided into three subclasses, depending of the degree of knowledge included in the learning dataset: supervised learning (full knowledge), semi-supervised learning (some pieces of information are not available) and unsupervised learning (no predefined class). Many ML algorithms have been proposed to handle these classification problems, from Naive Bayes algorithms to Artificial Neural Networks and Support Vector Machine algorithms. In this paper, we used the term "ML-based DDSSs" to refer to all the DDSSs using one of these ML algorithms.
According to their results, the radiologists were more favorable to double reading, even though most of them perceived that DDSSs were better at improving recall rates than double reading. The meta-analysis proposed by  Masud et al. (2019) , on the use of DDSSs in radiology departments, showed similar results on the low acceptability of DDSSs despite an improvement of performances perceived by radiologists. Only a handful of studies showed a good acceptability of Guideline-based DDSSs, in very specific situations. This is the case of  Porat et al. (2017) , who analyzed the acceptability by patients and physicians of a Guideline-based DDSS. 34 general practitioners participated in the study by consulting 12 standardized patients during controlled trials. The authors reported that 74% of GPs found the DDSS useful, even though the use of the DDSS required them to enter more data on patients while interacting with them.
For example, a technical error in programming could lead to an ill-advised recommendation. In such a case, one might argue that the true responsible is the programmer. But medical errors could also come from mistakes that a physician made when using the DDSS. The method on which the DDSS was based can also be a source of error. In the case of a Guideline-based DDSS using rules defined by experts, these "experts" might have provided rules that can be considered to be "dangerous" or "foolish" by the rest of the medical community. One might argue that ML-based DDSSs are more trustworthy than Guidelinebased DDSSs, due to the high performances of ML algorithms, outperforming physicians  (Esteva et al. , 2017) , and using large amounts of data. However, responsibility issues are not different in the case of ML-based DDSSs: if the ML-based DDSS's recommendation was wrong and led to a medical error, who was responsible? ML-based DDSSs are trained and evaluated on datasets that might fail to encompass all the variety of possible use cases.
Even if a trained ML-based DDSS had high sensitivity and specificity on a test dataset, these criteria of performances are not enough when we talk about patient safety in real situations. Moreover, supervised ML algorithms can only reproduce the behaviors they learned. Therefore, just like in the case of Guidelinebased DDSSs, if the learning dataset was based on the behaviors of physicians whose behavior can be considered to be "dangerous" or "foolish" by the rest of the medical community, the trained ML algorithm will reproduce, and even amplify, this "dangerous" behavior  (Garcia, 2016; Sandvig et al. , 2016; Zou and Schiebinger, 2018). The main difference with Guideline-based DDSSs is that it is more difficult for physicians to detect if a ML-based DDSS had an unwanted behavior, especially if the process of the ML-based DDSS is opaque to physicians.
Physicians and clinicians are accustomed to the use of such "gold-standard" guidelines, part of their work being to be aware of the new "gold-standard" for the cases they treat regularly. -Technology: Guideline-based DDSSs are generally integrated into already existing HISs and do not necessitate more technological resources than access to a database. -Processes: Guideline-based DDSSs generally necessitate that physicians enter symptoms of the patient or other additional data asked by the DDSS. In customary situations, the process of Guideline-based DDSSs can be redundant with the physicians' process during a consultation. -Objectives and values: the objective of Guideline-based DDSSs is generally to improve adherence to "gold-standard" guidelines. In customary situations, this objective is confronted with physicians' values, such as their free will, or the acceptability of "gold standard" guidelines  (Cabana et al.
-Management systems: It is possible to implement continuous learning by updating regularly the training dataset and rerunning the learning algorithm. This might also require to continuously test the performances of the DDSS, to prevent errors. However, none of these necessarily requires the intervention of an external agent, and everything can be automated. -Other resources: the understanding of recommendations by physicians, when it is possible, may generate additional workload. There certainly are exceptions to the general characteristics we explored above in our application of reality/design gaps analysis to Guideline-based DDSSs and MLbased DDSSs. Our goal was simply to highlight general trends in current ways to support physicians during their practices and see if they are applicable or not for customary consultations.
What is "information" in healthcare contexts? At first glance, one might think that the notion of "information" in our context is unequivocal. A piece of information, one might think, is a raw data formatted to be readable by a physician. The interpretation of a piece of information by a physician gives her/him pieces of knowledge about a situation and allows her/him to make a decision. Collected by EHRs and CPOEs, hospital databases are rich in such raw data on patients, including: weights, ages, symptoms, reviews of hospitalizations, drugs took, allergies, etc. All these raw data can give clinicians a first layer of information. With the same logic, the evolution of such data through time, their interconnection in patient care processes, gives a second layer of information. The notion of information hence appears more complex after all, since there are several layers of information.
An example of application In order to flesh out in concrete terms what such an "adjustive" approach consists in, we introduce here a practical example, referring to  Richard et al. (2018) , a work developed in collaboration with the public hospitals of Lyon to propose a DDSS dedicated to supporting customary consultations. Clinicians of the hospitals of Lyon have a software, called Easily R , at their disposal. This software allows clinicians to access different kinds of HISs. During consultations, physicians have access to the hospital's EHR and to CPOEs, but not to any DDSSs for now. Richard et al. (2018)  proposed an analysis focussed on interactions between physicians and patients, but also between physicians and HISs, during customary consultations of endocrinologists, to identify what kind of tools should provide relevant support in such situations.
Richard et al. (2018)  therefore set out to learn what data are needed by physicians by analyzing their searches and their entry in the hospital's database, so as to anticipate their needs and provide them with a subset of data about their current patient at the beginning of medical consultation. By doing so, the searching phase of medical consultations should be minimized by handing over to the information system the task for which it is more efficient than human beings: searching data in a large database. In this approach, the aim of decision support is to ensure that physicians have all the data they need on their patients, and the interpretation of these data is then left to physicians.
Acknowledgements This work was made in collaboration with employees of the hospitals of Lyon. Thanks to all of them. Special thanks to  Pr. Moulin  and  Dr. Riou  for their suggestions and instructive discussions. Special thanks also to  Juliette Rouchier ,  Olivier Cailloux , and  Philippe Grill  for their advices and comments on earlier versions of this manuscript. We also thank two anonymous reviewers of the journal for their powerful and exacting comments and criticisms.
Introduction Premenstrual syndrome (PMS) has the potential to affect the quality of life adversely. Published guidelines recommend the use of exercise as part of the first-line management interventions for PMS. However, the published evidence related to the effectiveness of physical activity and PMS is inconclusive. This review will assess the effectiveness of exercise-based interventions in reducing PMS in women screened or diagnosed with PMS in low and middle-income countries, where the prevalence of PMS is high. Methods and analysis Electronic databases will be researched, including Embase, Cochrane Central Register of Controlled Trials, MEDLINE, PsycINFO, Web of Science, ClinicalTrials. gov and Google Scholar. All the studies published until March 2020 will be included. A standardised data extraction form will be used adapted from the Cochrane Handbook of Systematic Reviews of Interventions.
Data sources and search strategy Multiple databases will be searched for the relevant research articles which include Embase, Cochrane Central Register of Controlled Trials, MEDLINE, PsycINFO, Web of Science, ClinicalTrials. gov and Google Scholar. The search strategy for this review will be developed by adapting, expanding, blending and updating search strategies from Cochrane Gynaecology and Fertility search strategies. 29 The search strategy will use the search terms presented in the online supplemental file 1. The researched articles will then be limited using the filters for LMICs and the English language. All the eligible studies published until March 2020 will be included. A search strategy developed for MEDLINE is shown in the online supplemental file 1. This search will be updated until the above-mentioned timeline.
Data extraction A standard data extraction form will be adapted from the Cochrane Handbook of Systematic Reviews of Interventions. 31 The data extraction will be carried out independently by three reviewers (SU, AK and PLLG) after piloting. The following data will be extracted from each selected study: author and year; the economic level of the country; study design; start date and end date; study duration; ethical approval obtained; population description; setting; sample size; lost to follow-up; PMS diagnosis method, time postdiagnosis of PMS; comorbidities; intervention type; timing or frequency of intervention; intervention provider and provider settings; compliance; time points measured and reported; questionnaire used; primary and secondary outcomes reported. The authors will be contacted in case of missing data.
Statistical analysis Data synthesis Data will be analysed using Review Manager V. 5. 3. As the primary outcome of this intervention is usually measured on a continuous scale, we will synthesise data retained from primary studies by meta-analysis using an inverse-variance random-effects method to report the standardised mean difference (SMD). The SMD will be used because we anticipate the PMS symptom scores to be reported using different tools such as the PSST or Penn Daily Symptom Report. The random-effects method will be used for this analysis to allow for between-study differences in intervention effects. A meta-analysis will be used only if the studies are sufficiently homogenous. If studies are not sufficiently homogenous (I 2 statistic >75. 0%), we will adopt a narrative synthesis. After discussing the baseline characteristics, types of intervention and outcomes considered in the studies, we will describe patterns across studies in terms of the magnitude and direction of any observed effects.
Outcome Measures The primary outcome parameter was the fecal calprotectin mean levels (µg/g of feces) at two and four weeks of age. Secondary outcomes were fecal AAT, elastase, neopterin and sIgA concentrations, gut microbiota, fecal pH. Gut permeability was evaluated by lactulose/mannitol ratio. Additionally, anthropometrics and body composition were measured. Additionally, serum ferritin and transferrin were analyzed. Finally, digestive tolerance and AEs were recorded. Baseline information was recorded during the first 48 h of birth, before any intake of the study formulas. This included the infants' and parents' demographic data and the infants' anthropometric measurements (weight, length, and head circumference). Parents/caregivers were given diaries where they recorded digestive tolerance (stool characteristics, spitting-up, and vomiting, and infants' behavior (crying, fussing or colic)) and any complementary feeding, AEs, or concomitant medication.
Each site was provided the same type of electrode for measuring fecal pH (Metrohm ® , Zofingen, Switzerland). pH meters were calibrated according to the manufacturer's specifications. To assess gut microbiota, fecal samples were suspended to a final concentration of approximately 0. 5 g stool/mL in saline solution containing 10% glycerol. The mixture was homogenized and stored at -20 to -80 • C until further analysis. The 16S rRNA gene variable region V1 to V3 was PCR amplified, sequenced on Roche 454 GS-FLX-Titanium sequencer and analyzed as previously described  [16]. Raw sequence data were deposited in the GenBank Short Read Archive. (Accession number: XXX). To assess gut permeability, urine samples were collected after 6 h ingestion of a lactulose/mannitol solution  [49]. Urinary concentrations of lactulose and mannitol were measured by HPLC.
Data were summarized as mean ± SD (or 95% confidence interval [CI]) or median and interquartile range (IQR). Weight, length, and head circumference were compared with WHO standards. Anthropometric data were also analyzed using a mixed model, with anthropometric data at birth, sex, treatment, visit, sex*visit interaction, and treatment*visit interaction as fixed effects and subjects as random effects. Likelihood ratio test was used to compare between groups. Mean daily frequency of spitting up, vomiting, colic, and sleep interruptions and mean daily number of hours of crying and lag of sleep, were analyzed by Poisson regression with days of observation introduced as an offset. Serious and non-serious AEs were analyzed using the chi square test. Calprotectin was approximately log-normally distributed, the measurements at week two and week four were averaged on the log-scale and then back transformed by applying the exponential function (the back transformed means are the geometric means).
t-test was used to compare between groups. In case of outliers, the Wilcoxon rank-sum test was performed, and the treatment difference was quantified using the Hodges-Lehmann estimator with 95% CI. Z-scores for biochemical markers were calculated relative to the breastfed infants using the mean and SD values from the breast-fed group. Statistical analyses were performed using SAS (SAS Institute Inc, Cary, NC, USA). For the analysis of the microbiota 16S rRNA genes pyrosequencing data, the diversity analyses and correlations with clinical data were performed with the website Calypso [50].
Ethics The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. It conformed to the International Conference on Harmonization guidelines for Good Clinical Practices and adhered to the applicable regulatory and legal requirements. The study was approved by the ethics committees of each institution: Lyon CPP Sud Est IV Ethics Committee and the French Agency for the Safety of Health Products (AFSSAPS) in France; Alexandra Hospital Scientific Council in Greece; and the Vienna Ethics Committee and the local regulatory authority (AGES) in Austria. The clinical trial has been registered on ClinicalTrials. gov on 10 September 2009 (NCT00984230).
The global differences between the bacterial communities of the formula fed groups and the breastfed physiological reference group were assessed by constrained multivariate analysis with hypothesis testing. The canonical correspondence analysis (CCA) at genus level separated the BF and FLPP groups from the F and FLP groups, with the data points for the FLPP formula group overlapping with the breastfed reference group at one (p = 0. 001) and four weeks (p = 0. 043) of age (Figure  4 , 1w and 4w). In contrast, at eight weeks of age after washout period with the commercial starter IF, all three formula groups were separated from the breastfed group (p = 0. 026, Figure  4, 8w ). The 16S rRNA gene sequencing dataset showed that B. animalis spp lactis, to which the added probiotic belongs, was mainly detected in samples of the FLP and FLPP supplemented intervention groups (Table  4 ) where they made only a minor contribution to the total Bifidobacterium population (Figure  3 ).
Association between Microbiota Profiles and Calprotectin Levels To investigate the role of the microbiota in the gut barrier maturation, we tested at each time point the association of the microbiota composition (genus level) with concomitant and subsequent calprotectin levels (defined as primary measure). The analysis considered the BF group only, the formulae groups only or all groups together. The microbiota profiles of the all groups dataset at four weeks were significantly associated with the calprotectin levels at four weeks (CCA, p = 0. 018) and eight weeks of age (CCA, p = 0. 026). In contrast, no significant association of fecal elastase levels with microbiota composition was observed. In univariate analysis, no clear association was observed between the genus abundances and calprotectin levels.
Acknowledgments: The authors wish to thank  E. Mallet ,  G. Putet ,  A. Pollak ,  J. C. Roze , and  E. Jumas-Bilack  for their medical expertise and support of the study,  J. Drai  for her work on the gut maturation index analysis and  K. Herkner  for his work on iron status. The authors are also grateful to  Julie Chenal  for her medical writing support.
P rediction is the bedrock of clinical practice. Inherent in every diagnosis is a prediction about the course of illness, and every prescription invokes a prediction about a response to treatment. For the most part, clinical predictions are made on a case-by-case basis based on a combination of experience and evidence. More recently, the uptake of electronic health records (EHRs), adoption of genomics technologies, and the advent of data science and machine learning have accelerated the development and publication of data-driven prediction models throughout medicine. Respiratory, sleep, and critical care medicine are no exception; prediction modeling has strong foundations in these fields, and they continue to be influential in its refinement and uptake. Journals are witnessing an increase in submissions related to prediction modeling.
MODEL ARCHITECTURE Considering Potential Predictor Variables How researchers decide on variables to include in a prediction model (also referred to as "features") is equally if not more important than the specific variables themselves. The large datasets increasingly used in biomedical research, such as those captured from EHRs, administrative systems, and high-dimensional "omics" platforms, include features that may number in the hundreds to thousands. Not all features available in modern datasets are practical or effective choices. There is a trade-off between the number of features included in a model and its capacity to generalize. This risk becomes particularly important when considering that associations between predictor and outcome may be idiosyncratic. A single center study may show bronchoscopies done on Tuesdays are predictive of lung cancer diagnosis, but this may simply reflect local practice in which the lung nodule clinic has access to the endoscopy suite on certain days.
The growing pervasiveness of smartphones and other devices, which can calculate complex scores automatically, has lessened the premium on "simple" scores with few variables, but clear trade-offs between parsimony and accuracy remain. For example, a prediction model for lung cancer that uses smoking history alone may be easy to use in any setting but may underperform compared with one based on smoking history, an exhaustive occupational history, and whole genome sequencing. Modern datasets may lack representation from demographic groups historically under-represented in biomedical research. Prediction models based on such datasets may lead to bias in The chronology of information is critical in prediction. A prediction, Ŷ, is made at time, t p , based on data collected up to and including that time, but no later.