Showing papers in "Journal of Clinical Epidemiology in 2021"

TL;DR: The PRISMA 2020 statement consists of updated reporting guidance for systematic reviews as discussed by the authors, which includes a survey conducted to inform the update, summarise decisions made at the PRISCMA update meeting, and describe and justify changes made to the guideline.

TL;DR: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement as discussed by the authors was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found.

TL;DR: New, interim guidance to support the conduct of rapid reviews (RRs) produced within Cochrane and beyond is offered in response to requests for timely evidence syntheses for decision-making purposes including urgent health issues of high priority.

TL;DR: Thematic analysis performed in this systematic scoping review has allowed for the creation of a suggested definition for rapid reviews that can be used to inform the systematic review community.

TL;DR: In this article, the authors present a framework for handling and reporting the analysis of incomplete data in observational studies, using a case study from the Avon Longitudinal Study of Parents and Children.

TL;DR: PCORnet’s infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases.

TL;DR: In this paper, the authors provide practical principles and examples to help GRADE users make optimal choices regarding their ratings of certainty of evidence using a minimally or partially contextualized approach, based on the GRADE clarification of evidence in 2017.

TL;DR: By identifying and overcoming challenges to the conduct and reporting of scoping reviews, reviewers may better ensure that scoping Reviews are effective in meeting the objectives of scoped reviews.

TL;DR: The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs.

TL;DR: This conceptual GRADE approach provides a framework for using evidence from models in health decision making and the assessment of certainty of evidence from a model or models and presents a summary of preferred terminology to facilitate communication among modelling and health care disciplines.

TL;DR: It is preferable to have multiple studies with imprecise estimates than having no study at all, and the justification to withhold an observational analysis of pre-existing data cannot be that the authors' estimates will be imprecising.

TL;DR: In this paper, a retrospective observational study was conducted to identify potentially eligible systematic reviews (SRs) indexed in PubMed from 2000 to 2019, and the authors observed a more than 20-fold increase in the number of SRs indexed over the last 20 years.

TL;DR: In this article, the authors evaluated the methodological and reporting quality of COVID-19 systematic reviews, summarizing and analyzing trends in their clinical topics, author countries, and study populations.

TL;DR: This article assessed the impact of restricting systematic reviews of conventional or alternative medical treatments or diagnostic tests to English-language publications on effect estimates and conclusions and found that restricting systematic review to English language publications appeared to have little impact on the effect estimates.

TL;DR: In this article, the authors suggest that most problems stem from an underlying paradox: although methodology is undeniably the backbone of high-quality and responsible research, science consistently undervalues methodology and the focus remains more on the destination (research claims and metrics) than on the journey.

TL;DR: In this article, the authors examined the effect of parameter uncertainty on prediction model performance and concluded that penalization methods are more reliable when needed most (i.e., when overfitting may be large) and recommend they are best applied with large effective sample sizes.

TL;DR: The Australian Guidelines for Care of People with COVID-19 provide an example of the feasibility of living guidelines, and an opportunity to test and improve living evidence methods.

TL;DR: Cochrane Crowd is sufficiently accurate and scalable to keep pace with the current rate of publication (and registration) of new primary studies, and has also proved to be a popular, efficient and accurate way for a large number of people to play an important voluntary role in health evidence production.

TL;DR: The proposed framework provides a reasonable basis for the development of methodological guidelines to deal with zero-events in meta-analysis and should be considered by researchers when making decisions on the selection of the synthesis methods in a meta- analysis.

TL;DR: A review of studies that had assessed the uptake of core outcome sets (COS) to explore the level of uptake across different COS and areas of health found a wide variation in uptake between the outcomes.

TL;DR: In this article, the authors implemented a systematic data-driven approach to identify predictors of non-response in the National Child Development Study (NCDS; 1958 British birth cohort), which can help make the missing at random assumption more plausible, which has implications for the handling of missing data.

TL;DR: In this article, the authors compared the effectiveness of various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models, and found that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low.

TL;DR: Authors should clearly report how they have derived the overall rating when applying AMSTAR 2 and reporting should allow for reproducing the overall ratings for editors, peer reviewers and readers.

TL;DR: In this paper, the authors present a primer for diagnostic and prognostic clinical prediction models, by discussing the basic terminology, some of the inherent challenges, and the need for validation of predictive performance and the evaluation of impact of these models in clinical care.

TL;DR: The authors conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods in oncology.

TL;DR: The probability of being cited seems associated with positive study outcomes, the authority of its authors and the journal in which that article is published.

TL;DR: The proposed graphical techniques may assist methodologists and authors in identifying overlap, which in turn may improve validity and transparency in OoSRs.

TL;DR: In this article, the authors systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields.

TL;DR: In this article, a correlation between adverse drug reactions relative risks estimated from meta-analyses and disproportionality analyses calculated from pharmacovigilance spontaneous reporting systems databases was found, and methodological choices modified this correlation.

TL;DR: The first article of a three-paper series introduces Evidence-Based Research as an approach to minimize unnecessary and irrelevant clinical health research that is unscientific, wasteful and unethical.