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Record W2102228952 · doi:10.1186/2046-4053-3-37

Evaluation of the Cochrane Collaboration’s tool for assessing the risk of bias in randomized trials: focus groups, online survey, proposed recommendations and their implementation

2014· article· en· W2102228952 on OpenAlexafffund
Jelena Savović, Laura Weeks, Jonathan A C Sterne, Lucy Turner, Douglas G. Altman, David Moher, Julian P. T. Higgins

Bibliographic record

VenueSystematic Reviews · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa HospitalUniversity of OttawaOttawa Regional Cancer Foundation
FundersCanadian Institutes of Health ResearchMedical Research CouncilNational Institute for Health and Care ResearchCancer Research UK
KeywordsMedicineUsabilityFocus groupRandomized controlled trialSystematic reviewMedical educationWorkloadMEDLINECochrane collaborationApplied psychologyFamily medicineCochrane LibraryPsychologyComputer scienceSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: In 2008, the Cochrane Collaboration introduced a tool for assessing the risk of bias in clinical trials included in Cochrane reviews. The risk of bias (RoB) tool is based on narrative descriptions of evidence-based methodological features known to increase the risk of bias in trials. METHODS: To assess the usability of this tool, we conducted an evaluation by means of focus groups, online surveys and a face-to-face meeting. We obtained feedback from a range of stakeholders within The Cochrane Collaboration regarding their experiences with, and perceptions of, the RoB tool and associated guidance materials. We then assessed this feedback in a face-to-face meeting of experts and stakeholders and made recommendations for improvements and further developments of the RoB tool. RESULTS: The survey attracted 380 responses. Respondents reported taking an average of between 10 and 60 minutes per study to complete their RoB assessments, which 83% deemed acceptable. Most respondents (87% of authors and 95% of editorial staff) thought RoB assessments were an improvement over past approaches to trial quality assessment. Most authors liked the standardized approach (81%) and the ability to provide quotes to support judgements (74%). A third of participants disliked the increased workload and found the wording describing RoB judgements confusing. The RoB domains reported to be the most difficult to assess were incomplete outcome data and selective reporting of outcomes. Authors expressed the need for more guidance on how to incorporate RoB assessments into meta-analyses and review conclusions. Based on this evaluation, recommendations were made for improvements to the RoB tool and the associated guidance. The implementation of these recommendations is currently underway. CONCLUSIONS: Overall, respondents identified positive experiences and perceptions of the RoB tool. Revisions of the tool and associated guidance made in response to this evaluation, and improved provision of training, may improve implementation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.759
metaresearch head score (Gemma)0.870
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.241
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7590.870
Meta-epidemiology (narrow)0.0070.008
Meta-epidemiology (broad)0.0130.023
Bibliometrics0.0370.026
Science and technology studies0.0040.007
Scholarly communication0.0130.016
Open science0.0100.013
Research integrity0.0130.010
Insufficient payload (model declined to judge)0.0100.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.796
GPT teacher head0.614
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations340
Published2014
Admission routes2
Has abstractyes

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