dataesr/scientific-paragraphs-categorization huggingface dataset:
Overfitting occurs when a model contains too many variables for the dataset to support. This results in a close fit to the data on which it was trained, but poor generalization to other datasets  (18) , undermining a key criterion of useful prediction. Any process that rigidly adheres to p value thresholds for variable selection poses similar risks of spurious conclusions  (19, 20). In the vast majority of scenarios, p value based feature selection methods are strongly discouraged  (21). Underscoring the rationale to eschew stepwise and p value screening, modern statistical approaches to feature selection (e. g. , penalized regression) avoid these pitfalls and can also inherently improve overall accuracy  (22). For these reasons, TRIPOD guidelines explicitly recommend these alternative procedures for prediction studies  (6).
g. , 50+ predictors) are often imposed on smaller datasets (e. g. , n = 300, with 30 outcomes) that cannot support them. Methods to determine how many predictors a model can accommodate are discussed elsewhere, and we encourage authors to consider what their datasets can actually support. For sample size guidance, we direct readers to methodological papers based on the type of outcome being predicted-continuous  (23, 24) , binary  (25, 26) , or time-to-event (25)-and recommend authors clearly indicate how they determined whether their dataset could support the chosen model.
Data Preprocessing Missing data frequently complicate prediction modeling. This is especially true when models are built with existing datasets, rather than those collected specifically for the purpose of model development. The former, though convenient, may contain gaps where key variables were not routinely collected or reliably recorded. Missing data can introduce bias, especially if not missing completely at random. Suppose we want to predict bleeding risk during an inpatient stay, and choose to include the admission international normalized ratio (INR) as a predictor. Our dataset includes this measure for 65% of patients. Is the remaining 35% missing completely at random? Since clinical presentation likely dictates whether the INR is measured at admission, the mere presence of an INR value (regardless of its result) carries information  (29).
If we include only patients with an admission INR measured, we may select a group with higher bleeding risk; these patients are likely on anticoagulants, or deemed by the clinician at high enough risk that an INR was ordered. By omitting patients without an INR we may bias the prediction model. Several strategies can address missing data. The approach using only patients with complete data (complete case analysis) can introduce bias and decreases sample size. This practice is therefore discouraged  (30). Another strategy is multiple imputation of missing values using methods described elsewhere  (31). The success of these approaches will depend on the methods used, the amount of missing data, and why the data are missing to begin with. Sometimes too much data are missing to support the proposed prediction modeling.
Model Validation Evaluating a model's predictive performance can be helpful during derivation in the fine tuning of its variables. However, such evaluation does not constitute justification for the model's adoption. This is because models overly adapted to the idiosyncrasies of a particular dataset may perform well in that dataset, despite having poor accuracy for new observations  (33). Many of the pitfalls described above will lead to overly optimistic performance models that fail to generalize. Validation refers to the process of confirming whether a prediction model generalizes to data that were not used in its development. Internal validation involves determining whether model performance is reproducible in the same underlying population (as distinct from the same specific sample) used to derive it  (33).
External validation determines whether a model is transportable by evaluating its performance in a population that is somehow distinct from the one used for derivation  (33). Generally, prediction models perform worse in new datasets compared with the sample in which they were developed. Models that perform without large decrements in accuracy in new datasets are more likely to generalize to other contexts we might care about-for example, clinical practice. Conversely, substantial decrements in performance suggest the model is overfitted. Emerging frameworks evaluate both the magnitude of and reasons for performance degradation  (34) (35) (36) ; such as the validation cohort being inherently different from the derivation cohort  (36). However, determining how much of a decrease in performance during validation is too much proves difficult and will likely depend on the specific study  (35).
The typical lifecycle of a prediction model thus involves progression through various stages of derivation and validation. Often, initial model descriptions may be based on validation in archival datasets. When entirely separate datasets are not available, a common compromise approach is to split a single dataset into two parts: a derivation cohort, and a separate validation cohort that is not used in developing the model itself. There are several strategies to handle a single dataset in this way, each with their own drawbacks and benefits (Supplemental Table  1 , Supplemental Digital Content 1, http://links. lww. com/CCM/F343) (37). We acknowledge that the distinction between internal and external validation is not always concrete, and will depend on study context. Nevertheless, a compelling validation must use the exact same model obtained from the derivation exercise.
Retraining on external data will yield an entirely new model, potentially leading to the same risks of overfitting that validation is meant to overcome. In the near future, we may see a new approach in which models are deliberately re-trained on local data  (38). This strategy might improve accuracy in the specific venue where the method is applied and may become more feasible as more health systems accrue the large datasets and How "usefulness" is operationally defined is subjective. AUROC = area under receiver operator characteristics curve.
In the era of big data, the distinction between clinical and statistical significance becomes particularly important. The width of CIs and the size of p values are both inversely proportional to the sample size of a dataset. As models developed from datasets with thousands of patients become more common, it is important to consider what a "significant" difference really means. In such datasets, a complex, 40-variable model with 85% accuracy may well be statistically distinguishable from a simple, two-variable model with 84% accuracy. We must then ask whether that difference is meaningful, and worth all the added complexity.
GUIDANCE FOR REPORTING Table  3  provides key components that must be present for readers to properly evaluate a prediction model. More detailed guidance for reporting prediction modeling studies is widely available. We encourage authors to refer to the TRIPOD checklist (https://www. tripod-statement. org) and ensure they include all recommended elements. Studies that leverage EHR derived datasets present unique considerations, and for these, we also encourage authors to refer to the Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) checklist (https://www. record-statement. org). Including these checklists in submissions is highly recommended. In general, reporting should be as transparent as possible, and should include full specification of statistical models and their diagnostics.
Recommended Practices Cautions Consider competing priorities of precision, parsimony, and transparency when approaching a prediction task. Prediction frameworks should not be used to make causal inferences. Think carefully about the prediction's intended purpose and prioritize feature selection elements as appropriate. Using p values from bivariable comparisons or stepwise procedures to select predictors leads to bias and overfitting. Report the prevalence and handling of missing data; consider steps other than case exclusion to address missing data. The size of a dataset, as well as the number of outcomes it contains, limit the number of predictor variables that the model can accommodate. Consider the expected nature of the relationships between predictors and the outcome (e. g. , linear, exponential, etc.
). Categorizing continuous variables can lead to loss of information. Conduct external validation to demonstrate a model can generalize to new observations. External validation should use the same model used to report the internal performance; avoid retraining on the external dataset. Seek reasonable comparators other than "no model" when evaluating model performance. Relying on the area under receiver operator characteristics curve alone can lead to an incomplete understanding of a model's performance. Follow appropriate reporting guidelines such as Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Reporting of studies Conducted using Observational Routinely-collected Data (RECORD).
predictive features in datasets with valuable information. The penalty is a function many candidate predictors. May include implausible features and of the coefficient's Does not require prior knowledge omit known predictive features. absolute magnitude about which features are pre- Individual predictors difficult to interpret. and a weight, λ. dictive. Performs poorly when predictors are If correctly weighted, can enhance highly correlated. prediction accuracy separate from Performs poorly when there are more use in feature selection. predictors than observations. Requires internal cross-validation to estimate λ. λ unstable in smaller datasets. Computationally intensive. Ridge (L2) Imposes a "penalty" to Computationally less intensive vs Not a true means of predictor selection: regularization bias predictor LASSO/ Elastic-Net.
penalization increasingly mild below coefficients toward Does not require prior knowledge 1. 0 and unable to exclude features zero. about which features are altogether. The penalty is a function predictive. Individual predictors difficult to interpret. of the coefficient's If correctly weighted, can enhance Requires internal cross-validation to squared magnitude prediction accuracy; outperforms estimate λ. and a weight, λ. LASSO if predictors are correlated. λ unstable in smaller datasets. Elastic-Net Imposes a "penalty" Can set coefficients to zero: can May include implausible features and (Mixed L1-L2) to bias predictor exclude nonpredictive features. omit features already known to be regularization coefficients toward Better handles correlated predictive. zero. predictors vs LASSO. Requires estimation of the optimal The penalty function When more predictors balance between L1/L2.
is a mix of both than observations, better Individual predictors difficult to LASSO and performance vs LASSO. interpret. Ridge, with a If correctly weighted, can enhance Requires internal cross-validation to weight between 0 prediction accuracy separate estimate λ. (complete Ridge) from use in feature selection. λ unstable in smaller datasets. and 1 (complete Does not require prior knowledge Computationally intensive. LASSO). about which features are predictive.
sensitivity. in irrelevant regions of (i. e. , the average pre- Higher is better: 1. 0 = Like AUROC, useful the model. cision). perfect precision; to visualize perform- Interpretation not intuitive. the overall event fre- ance over range of quency = noninform- possible thresholds. ative prediction. Hosmer- Calibration Observed probability vs Statistical hypothesis Easily represented Arbitrary grouping may Lemeshow predicted probability test-the null and interpreted poorly reflect risk Test across deciles of pre- hypothesis is that graphically. distributions. diction. the model fits the Graphing is itself use- May not detect subtle data. ful to show specific mis-calibrations in At high test statistics regions of risk mis- smaller datasets. (low p values), reject specification. May be overly conservative the null.
in larger datasets. Only assesses calibration; incomplete view of performance. Net benefit Utility Number of true Higher net benefit Allows (unequal) (decision positives minus indicates more weighting of false curve weighted number utility at a given positives vs false analysis) of false positives, probability negatives. divided by sample threshold. Reflects the size, plotted over usefulness of any range of threshold individual model probabilities. prediction. Weight is the ratio of harm to benefit.
Catheters For the current study we included only data related to AC. All study centers complied with the French recommendations for catheter insertion and care, which are similar to CDC recommendations  [8] : (1) maximal sterile barrier precautions (large sterile drape; surgical hand antisepsis; and mask, cap, sterile gloves, and gown); (2) the site of insertion was left to the discretion of the physician caring for the patient; (3) alcoholic povidone iodine solution or chlorhexidine gluconate was used for skin antisepsis at catheter insertion and during dressing changes; (4) semipermeable chlorhexidine-impregnated or standard dressing was used at all insertion sites and was changed 24 h after catheter insertion and then every 3 or 7 days according to standard practice in each ICU. Leaking, soiled, or wet dressings were changed immediately.
This model takes into account the censored nature of the data and possible intra-cluster dependence using a robust sandwich covariate estimate. Analyses were stratified by ICU and data were censored at 28 days since catheter insertion. Hazard risk for MCRI, CR-BSI and catheter colonization was evaluated by univariate and multivariate analysis. The variable "ultrasound guidance" (US vs. non-US) was forced in our multivariate models and the other variables showing significance in the univariate analysis were used as adjustment factors. The choice of adjustment variables was based on the results of the univariate analysis and refined by including clinically relevant variables (i. e. , Simplified Acute Physiology Score [SAPS] II score). The proportionality of hazard risks for catheter type was tested using Martingale residuals.
To date, the role of US in the context of intravascular arterial catheter infection is not clear. The use of an additional device, such as ultrasound, may complicate AC insertion and set the stage for breaches in aseptic non-touch technique, and the gel used for optimizing visibility may increase the risk of catheter infection  [22, 23]. However, a shorter insertion time and fewer insertion attempts may counterbalance the risk for infection. In an environment of consistent catheter care representing the largest dataset ever assembled, we showed that the US did not increase the infectious risk among ACs. No differences were observed according to the body site of insertion, femoral or radial, and the skin colonization at removal was similar between the two groups. Therefore, our results support the growing evidence that recommends using US routinely  [1, [3] [4] [5].
Our study has several limitations. First, its design is observational and the US utilization was not randomized. However, we presented exhaustive data that have been prospectively collected by trained investigators and study monitors during RCTs, and we adjusted our analyses on major confounders. Second, no data on the US type (e. g. , Doppler vs. ultrasound guidance) or sterile sheath used were included. Third, all RCTs were conducted in University-affiliated ICUs in France from 2011 to 2014, thus limiting the generalizability of the results. However, aseptic insertion techniques have not been changed since 2014. Fourth, a post-hoc analysis of our dataset showed a 22. 5% probability that US was associated with a falsely significant increased HR of ≥ 1. 1 for MCRI (18% for colonization). Fifth, no information on the number of attempts and other mechanical complications were included.
La majorité des médicaments de notre liste figure dans les listes Start & stop 2014 (133 MPI, 72 %), Beers 2012 (68, 37 %) et Laroche (105 MPI, 57 %). Enfin, on retrouve 35 de nos MPI (19 %) dans la liste Allemande Prescus et 26 (14 %) dans la liste Mcleod et la base de données finlandaise Fiméa. La majorité des MPI restant ont un SMR important (51,3 %), 12 % avec SMR modéré et 20 % ont un SMR faible ou insuffisant. Par ailleurs, nous avons rajoutées les recommandations de la HAS (AMI) pour les médicaments du système cardiovasculaire et du système nerveux (Annexe 1). Les MPI retirés sont répartis entre les différentes classes médicamenteuses : 36 médicaments du système nerveux, 21 du système cardiovasculaire, 11 médicaments du système respiratoire, 9 médicaments des voies digestives et métabolisme, 9 médicaments du système musculosquelettique, 1 médicament du système génito-urinaire et hormones sexuelles et 1 médicament du système génito-urinaire et hormones sexuelles.
Les médicaments retirés de la EU(7)PIM et le motif du retrait )PIM (critères explicites) : 282 médicaments 183 médicaments Indications 183 médicaments Commercialisés en France Recommandations HAS AMI (critères implicites) Figure 1. Flowchart pour la sélection des MPI Tableau 1. Listes de médicaments potentiellement inappropriés (MPI) chez les personnes âgées selon les pays Pays Auteurs Année Nombres de MPI Finlande, Estonie, France, néerlandais, Espagne, Suisse (EU(7)PIM Guiteras et al. 2015 282 France (STOPP/START VF) Lang et al 2015 115 Finlande 2013 Base de données Meds75 + contenant 500 médicaments utilisées chez les personnes âgées Etats-Unis Beers et al. 2012 (dernière mise à jour depuis 1991) 53 Autriche Mann et al. 2012 61 Taiwan Chang et al. 2012 36 Allemagne (PRESCUS list) Holt et al.
Background: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention's effects.
Results: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.
Trial sequential analysis as a method can be performed using the Trial Sequential Analysis software, which is freely available alongside its user manual online at The Copenhagen Trial Unit website  [6]. Here we employ Trial Sequential Analysis and combine data from feasibility and pilot RCTs testing a text message-based smoking cessation intervention for pregnant women ('MiQuit')  [7, 8]  to estimate the sample size that one or more future RCTs would need to recruit, to provide a more decisive answer regarding the effect of the intervention. We also show how data from the new, planned trial or trials can be combined with data from earlier trials using Trial Sequential Analysis to assess the intervention's benefits and harms. Using Trial Sequential Analysis sample size estimation methods maximises use of available trial data and consequently, the new RCT or trials may become smaller than they would have been using conventional sample size estimation methods.
Trial sequential analysis Meta-analyses aim to discover the benefit or harm of an intervention as early and as reliably as possible. As a result, they tend to be updated when new trials are published  [19]. When intervention evaluation has just begun and only few, smaller trials are available, meta-analyses may be conducted on sparse amounts of data and are at high risk of random type I and type II errors  [20]. As meta-analyses are updated they are subjected to repeated significance testing, which increases the risk of type I errors  [21]. When there are few data available, the Trial Sequential Analysis software resolves these issues by having stringent thresholds for assessing statistical significance, using monitoring boundaries. Monitoring boundaries also take into account the volume of significance testing which has been undertaken through adjusting the thresholds that are used to define whether or not results are considered statistically significant  [6].
In the Trial Sequential Analysis software, trials are chronologically ordered, and interim analyses are conducted as each trial is added using summary data from each trial. In a trial sequential analysis where the 'required information size' has not been reached, the threshold for statistical significance is inflated to account for sparse data and multiple testing of the interim analyses using monitoring boundaries; thus, the 95% confidence interval is not providing coverage of the real uncertainty and the cut-off for determining statistical significance is below the usual nominal figure of 0. 05  [18]. Furthermore, the Trial Sequential Analysis software provides adjusted confidence intervals if the 'required information size' has not been reached, which we refer to as Trial Sequential Analysis-adjusted confidence intervals  [18].
Technical details regarding how monitoring boundaries, information size, and Trial Sequential Analysis-adjusted confidence intervals are calculated can be found elsewhere  [6, 18]. Other statistical software, such as STATA and packages in R, could potentially be programmed to perform trial sequential analysis however to our knowledge these have only been performed on hazard ratios for time-to-event data  [25, 26]. In the worked examples below, we show how the Trial Sequential Analysis software can be used to estimate the sample size required for one or more new trials to add further data to a meta-analysis to provide more firm evidence for an intervention either having or not having the postulated effect.
Consequently, the estimated required information size of 1296 participants is only slightly different to that using conventional sample size estimation due to rounding errors. The estimate would be larger if heterogeneity were present. As the cumulative z-curve does not cross the upper trial sequential monitoring boundary for benefit, this Trial Sequential Analysis shows that further information is required before any firm conclusion can be reached about MiQuit efficacy. Although the conventional metaanalysis suggested, with borderline significance, that pregnant women randomised to MiQuit were more than twice as likely to be abstinent from smoking in late pregnancy, the Trial Sequential Analysis software shows that this finding is not sufficiently robust. The Trial Sequential Analysis-adjusted confidence intervals for cessation using MiQuit (pooled OR 2.