Systematic Reviewers Research Articles

The SUPER reporting guideline suggested for reporting of surgical technique.

Existing reporting guidelines pay insufficient attention to the detail and comprehensiveness reporting of surgical technique. The Surgical techniqUe rePorting chEcklist and standaRds (SUPER) aims to address this gap by defining reporting standards for surgical technique. The SUPER guideline intends to apply to articles that encompass surgical technique in any study design, surgical discipline, and stage of surgical innovation. Following the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network approach, 16 surgeons, journal editors, and methodologists reviewed existing reporting guidelines relating to surgical technique, reviewed papers from 15 top journals, and brainstormed to draft initial items for the SUPER. The initial items were revised through a three-round Delphi survey from 21 multidisciplinary Delphi panel experts from 13 countries and regions. The final SUPER items were formed after an online consensus meeting to resolve disagreements and a three-round wording refinement by all 16 SUPER working group members and five SUPER consultants. The SUPER reporting guideline includes 22 items that are considered essential for good and informative surgical technique reporting. The items are divided into six sections: background, rationale, and objectives (items 1 to 5); preoperative preparations and requirements (items 6 to 9); surgical technique details (items 10 to 15); postoperative considerations and tasks (items 16 to 19); summary and prospect (items 20 and 21); and other information (item 22). The SUPER reporting guideline has the potential to guide detailed, comprehensive, and transparent surgical technique reporting for surgeons. It may also assist journal editors, peer reviewers, systematic reviewers, and guideline developers in the evaluation of surgical technique papers and help practitioners to better understand and reproduce surgical technique. https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#SUPER.

Annual Review of Ecology, Evolution, and Systematics, Volume 53

About This Journal—The Annual Review of Ecology, Evolution, and Systematics, in publication since 1970, covers significant developments in the fields of ecology, evolutionary biology, and systematics, as they apply to all life on Earth. Essay reviews cover topics ranging from phylogeny, speciation, and molecular evolution through behavior and evolutionary physiology to population dynamics, ecosystems processes, and applications in invasion biology, conservation, and environmental management. 20 articles in Volume 53.

Annual Review of Ecology, Evolution, and Systematics, Volume 52

About This Journal—The Annual Review of Ecology, Evolution, and Systematics, in publication since 1970, covers significant developments in the fields of ecology, evolutionary biology, and systematics, as they apply to all life on Earth. Essay reviews cover topics ranging from phylogeny, speciation, and molecular evolution through behavior and evolutionary physiology to population dynamics, ecosystems processes, and applications in invasion biology, conservation, and environmental management. 30 articles in Volume 52.

Prognostic factors for persistent symptoms in adults with mild traumatic brain injury: an overview of systematic reviews

BackgroundMild traumatic brain injury (mTBI) is an increasing public health problem, because of its persistent symptoms and several functional consequences. Understanding the prognosis of a condition is an important component of clinical decision-making and can help to guide the prevention of persistent symptoms following mTBI. The prognosis of mTBI has stimulated several empirical primary research papers and many systematic reviews leading to the identification of a wide range of factors. We aim to synthesize these factors to get a better understanding of their breadth and scope.MethodsWe conducted an overview of systematic reviews. We searched in databases systematic reviews synthesizing evidence about the prognosis of persistent symptoms after mTBI in the adult population. Two reviewers independently screened all references and selected eligible reviews based on eligibility criteria. They extracted relevant information using an extraction grid. They also rated independently the risk of bias using the ROBIS tool. We synthesized evidence into a comprehensive conceptual map to facilitate the understanding of prognostic factors that have an impact on persistent post-concussion symptoms.ResultsFrom the 3857 references retrieved in a database search, we included 25 systematic reviews integrating the results of 312 primary articles published between 1957 and 2019. We examined 35 prognostic factors from the systematics reviews. No single prognostic factor demonstrated convincing and conclusive results. However, age, sex, and multiple concussions showed an affirmatory association with persistent post-concussion outcomes in systematic reviews.ConclusionWe highlighted the need for a comprehensive picture of prognostic factors related to persistent post-concussion symptoms. We believe that these prognostic factors would guide clinical decisions and research related to prevention and intervention regarding persistent post-concussion symptoms.Systematic review registrationPROSPERO CRD42020176676

An Investigation of Mathematics Education Studies Conducted with Turkish Primary Teachers

The studies conducted with in-service and preservice primary teachers in the field of mathematics teaching are among the research areas that are important and will not lose their importance soon as the outcomes of these research areas are critical for teaching mathematics in primary education and restructuring teacher education programs. As the number of studies in this field conducted in recent years increases, the necessity of reference research that will guide future studies and evaluate the status of current ones comes to the front. Using the method of systematic reviewing, research studies on mathematics education, which are published in the journals indexed in ULAKBİM, conducted between 2010-2021 years, and conducted with in-service and preservice primary teachers in Turkey, were examined. A total of 100 research studies were analyzed by using content analysis. All the studies were examined focusing on their research area, learning area, research method, data collection tools, sample and sample size, and data analysis methods. This study aimed to reveal the research trends in Turkey first. The secondary goal of the study is to guide future research studies by revealing what is highlighted and missing in the field. Considering research results, researchers can expand and improve research in primary mathematics education.

Early diagnosis for pulmonary embolism: A systematic review and meta-analysis.

The incidence of acute pulmonary embolism (APE) (especially early diagnosis) has increased annually in recent years, but the diagnosis of APE is a great challenge for every clinician. However, few studies have evaluated multiple diagnostic indicators simultaneously. A systematic search was performed using CNKI, Wan fang data, VIP, PubMed and Web of Science for studies on the diagnosis of pulmonary embolism published up to October 31, 2022. Using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), we evaluated the risk of bias in included studies, and used a random-effects meta-analysis to obtain the summary sensitivity and specificity. The data that were extracted and calculated for this study included the first author, year of publication, country, study type, sample size, disease type, gold standard, diagnostic indicators and 4-compartment table data. We strictly followed the Preferred Reporting Items for Systematics reviews and Meta-Analysis (PRISMA) guidelines in this review. This study included 30 articles with a total sample size of 8947 cases, involving 4 detection methods: D-dimer, Geneva rules, Wells rules, and lung imaging. The combined effect size showed that lung imaging had the highest diagnostic value (SEN = 0.95, SPE = 0.89), followed by D-dimer (SEN = 0.92, SPE = 0.60), Geneva rules (SEN = 0.78, SPE = 0.68), and Wells rules (SEN = 0.77, SPE = 0.67). The area of lung imaging was largest under the Summary Receiver Operator Characteristic (SROC) curve (AUC = 0.97), followed by Geneva rules (AUC = 0.80), Wells rules (AUC = 0.79), and D-dimer (AUC = 0.74). All 4 detection methods showed good ability to diagnose PE, and lung imaging was the best. Clinical trials are recommended to build an early decision-making model for the diagnosis of pulmonary embolism in order to increase the detection rate and improve prognosis.

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model’s performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the past 15 years. We start by categorizing the current threat models and attacks and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning and shed light on the current limitations and open research questions in this research field.

A structured approach to information retrieval improved identification of funding and researchers’ conflicts of interest in trials included in Cochrane reviews

ObjectivesTo compare the contemporary Cochrane review approach for retrieving information on trial funding and researchers’ conflicts of interest with a structured approach for information retrieval. Study Design and SettingMethodological study of 100 Cochrane reviews from August to December 2020 and one randomly selected trial from each review. Reporting of trial funding and researchers’ conflicts of interest in reviews was compared with information identified using a structured retrieval process, and time to retrieve information was noted. We also formulated a guide to systematic reviewers for efficient information retrieval. ResultsSixty-eight of 100 Cochrane reviews reported trial funding and 24 reported trial researchers’ conflicts of interest. A simple structured approach, searching only trial publications (including conflicts of interest disclosure forms), identified funding for 16 additional trials and conflicts of interest information for 39 additional trials. A comprehensive structured approach, searching multiple information sources, identified funding for two additional trials and conflicts of interest for 14 additional trials. The median time to retrieve information was 10 minutes per trial (interquartile range: 7–15) for the simple approach and 20 minutes (11–43) for the comprehensive approach. ConclusionA structured information retrieval approach improves identification of funding and researchers’ conflicts of interest in trials included in Cochrane reviews.

Implementation Barriers of Multidisciplinary Care in Chronic Kidney Disease Through a CFIR Framework: a Narrative Review

Introduction 37 million Americans suffer from chronic kidney disease, which affects multiple organ systems and requires multidisciplinary care. Multidisciplinary care is an inherently broad and complex topic, and while it is being implemented across health care in the United States and abroad, multidisciplinary care outcomes are poor in this patient population. It is possible that there exist gaps in the literature regarding implementation and replication of multidisciplinary care interventions such that health care practices are unable to fully take advantage of multidisciplinary care publications for chronic kidney disease. This narrative review utilizes the five domains of the Consolidated Framework for Implementation Research to address barriers to multidisciplinary care implementation for chronic kidney disease. Methods A systematized review of peer-reviewed literature including systematic reviews and meta-analyses related to chronic kidney disease and multidisciplinary care through January 1, 2021 was conducted. The five interventions with the most barriers qualitatively identified were analyzed. Results Twelve potentially eligible reviews were identified, and 5 unique systematic reviews and meta-analyses were selected for a total of 48 articles, and ultimately, 5 articles were selected for inclusion. Based on the Consolidated Framework for Implementation Research which includes 5 domains of barriers, we discussed barriers of implementation in all 5 domains within the 5 articles. Discussion Because it is essential that multidisciplinary care for patients with chronic kidney disease be improved and implemented to the fullest extent, researchers should be aware of barriers to implementation and publish results by taking into account the Consolidated Framework for Implementation Research.

Assessing the Integrity of Clinical Trials Included in Evidence Syntheses.

Evidence syntheses of randomized clinical trials (RCTs) offer the highest level of scientific evidence for informing clinical practice and policy. The value of evidence synthesis itself depends on the trustworthiness of the included RCTs. The rising number of retractions and expressions of concern about the authenticity of RCTs has raised awareness about the existence of problematic studies, sometimes called "zombie" trials. Research integrity, i.e., adherence to ethical and professional standards, is a multi-dimensional concept that is incompletely evaluated for the RCTs included in current evidence syntheses. Systematic reviewers tend to rely on the editorial and peer-review system established by journals as custodians of integrity of the RCTs they synthesize. It is now well established that falsified and fabricated RCTs are slipping through. Thus, RCT integrity assessment becomes a necessary step in systematic reviews going forward, in particular because RCTs with data-related integrity concerns remain available for use in evidence syntheses. There is a need for validated tools for systematic reviewers to proactively deploy in the assessment of integrity deviations without having to wait for RCTs to be retracted by journals or expressions of concern issued. This article analyzes the issues and challenges in conducting evidence syntheses where the literature contains RCTs with possible integrity deficits. The way forward in the form of formal RCT integrity assessments in systematic reviews is proposed, and implications of this new initiative are discussed. Future directions include emphasizing ethical and professional standards, providing tailored integrity-specific training, and creating systems to promote research integrity, as improvements in RCT integrity will benefit evidence syntheses.

Systematic reviewers used various approaches to data extraction and expressed several research needs: a survey

ObjectiveData extraction is a prerequisite for analyzing, summarizing, and interpreting evidence in systematic reviews. Yet guidance is limited, and little is known about current approaches. We surveyed systematic reviewers on their current approaches to data extraction, opinions on methods, and research needs. Study Design and SettingWe developed a 29-question online survey and distributed it through relevant organizations, social media, and personal networks in 2022. Closed questions were evaluated using descriptive statistics, and open questions were analyzed using content analysis. Results162 reviewers participated. Use of adapted (65%) or newly developed extraction forms (62%) was common. Generic forms were rarely used (14%). Spreadsheet software was the most popular extraction tool (83%). Piloting was reported by 74% of respondents and included a variety of approaches. Independent and duplicate extraction was considered the most appropriate approach to data collection (64%). About half of respondents agreed that blank forms and/or raw data should be published. Suggested research gaps were the effects of different methods on error rates (60%) and the use of data extraction support tools (46%). ConclusionSystematic reviewers used varying approaches to pilot data extraction. Methods to reduce errors and use of support tools such as (semi-)automation tools are top research gaps.

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

BackgroundThe COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process.MethodsIn this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article.ResultsThe ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset.ConclusionThis study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

Launching “methods and statistics tutorials”: A collection of resources for systematic reviewers

Robust and innovative methods are the lynchpin of Cochrane and remain the basis of its reputation as the home of high-quality systematic reviews. As methodology evolves to respond to the evidence needs of our stakeholders, it is crucial for Cochrane to be able to offer timely and clear advice to those working on our reviews. Cochrane's Methods Support Unit (MSU) was established in 2019 [1] to provide methodological and statistical support to authors and editors working on Cochrane protocols and reviews. A key challenge in the first 2 years of the Unit was the introduction of our new tool [2] to assess the risk of bias in randomized controlled trials (RCTs), but we have dealt with a multiplicity of different issues since being established. By March 2023, the Unit has dealt with over 1200 referrals relating to protocols, systematic reviews, updates of reviews, and statistical and methodological queries relating to reviews. Our work has ranged from full methodological and statistical reviews of research incorporating network meta-analyses to individual queries about the use of an intracluster correlation coefficient to adjust data from an individual study. It quickly became apparent that similar queries were often raised and we noticed common problems when working on reviews. Our monthly web clinic series was set up to allow authors and editors to raise questions and then later to provide guidance on a specific topic in addition [3]. However, we also felt that there is a need for accessible help with these issues which would be instantly available. Professor Doug Altman was a leader and pioneer in Cochrane and in the field of medical statistics [4]. One of Professor Altman's legacies was the Statistics Notes series in The BMJ, which provided clear advice on statistical concepts to medical researchers. Inspired by this work, former and current MSU managers (Dr Kerry Dwan and Rachel Richardson) are launching this series to provide easily accessible advice to the evidence synthesis community on the common methodological and statistical issues that we have observed within Cochrane. The hope is to make this as interactive and easy to understand as possible. In addition, the series will collaborate with Cochrane Training [5] to complement the articles in the series with visual learning, such as short videos or quick e-learning checks, to help those who learn better by doing or watching rather than reading. There are various sources of advice on methodological and statistical issues already available to reviewers, including the Cochrane Handbook for Systematic Reviews of Intervention [6] and resources from specialized Cochrane Methods Groups. This series will complement existing sources by responding to the day-to-day difficulties encountered by reviewers when implementing systematic review methods. The MSU is in a unique position to provide advice; having dealt with thousands of queries and requests for peer review since our inception, we know what reviewers find difficult and also why these issues cause confusion. We also believe that linking the articles with resources from Cochrane Training will make this series uniquely useful. dealing with data from crossover RCTs; the correct way to undertake and interpret subgroup analyses; inclusion of multiarm trials in meta-analysis; heterogeneity; the use of fixed versus random effects models in meta-analysis. Early articles in the series will focus on the use of effect measures in systematic reviews, specifically the standardized mean difference and the use of risk ratios versus odds ratios. These two topics often cause difficulties for authors and editors. Examples will be given in the articles of when to use these measures, how to calculate them and their interpretation. We will also cover cluster RCTs early in the series. We welcome proposals for this series from members across the evidence synthesis community on areas they see as methodological and statistical challenges for authors. Articles should be short tutorials, use the Commentary article type, and, in general, be less than 1000 words. Cochrane's mission is to produce trusted synthesized evidence, make it accessible to all, and advocate for its use. Ensuring that evidence synthesis methods are implemented wisely and accurately is foundational to us being able to achieve this mission. With the Methods and Statistics Tutorials series, we hope to support and onboard a wider and more diverse community of authors to produce the next generation of high-quality Cochrane Reviews and other evidence syntheses. Kerry Dwan: Conceptualization; writing—original draft; writing—review and editing. Rachel Richardson: Conceptualization; writing—review and editing. Kerry Dwan and Rachel Richardson are former and current employees, respectively, of Cochrane. None The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/cesm.12017. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

The need for robust critique of arts and health research: Dance-movement therapy, girls, and depression.

We examine a highly cited randomized controlled trial on dance-movement therapy with adolescent girls with mild depression and examine its treatment in 14 evidence reviews and meta-analyses of dance research. We demonstrate substantial limitations in the trial which seriously undermine the conclusions reached regarding the effectiveness of dance movement therapy in reducing depression. We also show that the dance research reviews vary substantially in their treatment of the study. Some reviews provide a positive assessment of the study and take its findings at face value without critical commentary. Others are critical of the study, identifying significant limitations, but showing marked differences in Cochrane Risk of Bias assessments. Drawing on recent criticisms of systematic reviewing and meta-analysis, we consider how reviews can be so variable and discuss what is needed to improve the quality of primary studies, systematic reviews, and meta-analyses in the field of creative arts and health.

Black Health in Canada: Protocol for a Scoping Review.

Black Canadians experience poor health care, poor health outcomes, and a greater burden of health inequalities, much of which is rooted in the unequal distribution of social determinants of health. Despite Canada's emphasis on social inclusion, Canada's Black population faces substantial social inequities that affect their health and well-being. These disparities may specifically be attributed to racial discrimination, immigration status, precarious housing, underemployment, and increased poverty among Black Canadians. This paper describes a protocol for a scoping review that aims to understand the range and nature of research conducted on the health of Black Canadians as well as the gaps in this literature. Arksey and O'Malley's methodological framework guided the conduct of the scoping review. We searched electronic databases (CINAHL, Embase, Global Health, MEDLINE, PsycINFO, Scopus, Sociological Abstracts, and Web of Science) and grey literature sources for peer-reviewed articles and grey reports on the health of Black Canadians. Six reviewers independently screened the abstracts and full text of studies to determine eligibility for inclusion. According to the PRISMA-ScR (Preferred Reporting Items for Systematics Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, the findings will be synthesized quantitatively and qualitatively through thematic analysis. Title, abstract, and full-text screening concluded in October 2022. Data collection is in progress and is expected to be completed by April 2023. Data analysis and drafting of the manuscript will be done thereafter. Findings from the scoping review are expected to be provided for peer review in 2023. This review will collect important data and evidence related to the health (mental, reproductive, and sexual; social determinants of health) of the Black population in Canada. The findings could help identify existing gaps in the health of Black individuals in Canada and inform future research paradigms. The findings will further inform the development of a knowledge hub on Black Canadians' health. PRR1-10.2196/42212.

Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.

This study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies. Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance. Lighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone. The study's findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.

Improving systematic reviews: guidance on guidance and other options and challenges

Multiple guideline tools are available for systematic reviews. These tools intend to standardize protocol development, require comprehensive reporting, and improve methodological rigor, including risk of bias assessment of primary studies and appraisal of the conduct of the reviews themselves. We recently published a guidance paper concerning these instruments and we hope that it will prove useful to producers, appraisers, and users of systematic reviews. There are still numerous open frontiers in improving systematic reviews. These include but are not limited to training of systematic reviewers; education of peer-reviewers, editors, and publishers; improving funder-based incentives, diminishing redundancy, increasing transparency, requiring protocol registration, confirming reporting and conduct standards, and establishing expectations of current meta-analysis methods. Each of these issues has caveats and challenges. Moreover, too many influential reviews continue to be non-systematic and expert opinion based. We need to understand why these reviews continue to be favored in the literature. Additional opportunities and need for research arise in the connection between primary evidence and systematic reviews. In some cases, the two may become indistinguishable. Living reviews become increasingly attractive in the currently evolving research circumstances but require additional safeguards. The connection between systematic reviews and guidelines or other implementation and decision-making tools is transitioning as well. Guidance efforts gain increasing attention, and may indeed help improve evidence synthesis but proper meta-research is needed to rigorously assess any improvements. We should maximize the contribution of systematic reviews, but also reduce the chances of producing checklist-heavy, ritual-burdened documents of questionable utility.

Assessing and minimizing risk of bias in randomized controlled trials of tobacco cessation interventions: Guidance from the Cochrane Tobacco Addiction Group.

The Cochrane Tobacco Addiction Group has created risk of bias tools, which are topic-agnostic. In 2012 the Cochrane Tobacco Addiction Group created guidance specific to considerations for reviews of randomized controlled trials of tobacco cessation interventions, building on existing Cochrane tools. The guidance covers issues relating to selection bias, performance bias, detection bias, attrition bias and selective reporting. In this paper, we set out to make this guidance publicly available, so that others can use and cite it. We provide advice for using this tool to appraise trials critically as a systematic reviewer. We also provide guidance for triallists on ways to use this tool to improve trial design and reporting.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Translation into Russian

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: https://orcid.org/0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015;162:W1-W73. doi: https://doi.org/10.7326/M14-0698].

GRADE concept 4: rating the certainty of evidence when study interventions or comparators differ from PICO targets

ObjectivesThis Grading of Recommendations Assessment, Development and Evaluation (GRADE) concept article offers systematic reviewers, guideline authors, and other users of evidence assistance in addressing randomized trial situations in which interventions or comparators differ from those in the target people, interventions, comparators, and outcomes. To clarify what GRADE considers under indirectness of interventions and comparators, we focus on a particular example: when comparator arm participants receive some or all aspects of the intervention management strategy (treatment switching). Study Design and SettingAn interdisciplinary panel of the GRADE working group members developed this concept article through an iterative review of examples in multiple teleconferences, small group sessions, and e-mail correspondence. After presentation at a GRADE working group meeting in November 2022, attendees approved the final concept paper, which we support with examples from systematic reviews and individual trials. ResultsIn the presence of safeguards against risk of bias, trials provide unbiased estimates of the effect of an intervention on the people as enrolled, the interventions as implemented, the comparators as implemented, and the outcomes as measured. Within the GRADE framework, differences in the people, interventions, comparators, and outcomes elements between the review or guideline recommendation targets and the trials as implemented constitute issues of indirectness. The intervention or comparator group management strategy as implemented, when it differs from the target comparator, constitutes one potential source of indirectness: Indirectness of interventions and comparators—comparator group receipt of the intervention constitutes a specific subcategory of said indirectness. The proportion of comparator arm participants that received the intervention and the apparent magnitude of effect bear on whether one should rate down, and if one does, to what extent. ConclusionTreatment switching and other differences between review or guideline recommendation target interventions and comparators vs. interventions and comparators as implemented in otherwise relevant trials are best considered issues of indirectness.