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Record W2970684805 · doi:10.1136/bmj.l4898

RoB 2: a revised tool for assessing risk of bias in randomised trials

2019· letter· en· W2970684805 on OpenAlexafffund
Jonathan A C Sterne, Jelena Savović, Matthew J. Page, Roy G. Elbers, Natalie Blencowe, Isabelle Boutron, Christopher J Cates, Hung‐Yuan Cheng, Mark Corbett, Sandra Eldridge, Jonathan Emberson, Miguel A. Hernán, Sally Hopewell, Asbjørn Hróbjartsson, Daniela R. Junqueira, Peter Jüni, Jamie J Kirkham, Toby J Lasserson, Tianjing Li, Alexandra McAleenan, Barnaby C Reeves, Sasha Shepperd, Ian Shrier, Lesley Stewart, Kate Tilling, Ian R. White, Penny Whiting, Julian P. T. Higgins

Bibliographic record

VenueBMJ · 2019
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcGill UniversityJewish General HospitalSt. Michael's HospitalUniversity of Alberta
FundersNational Eye InstituteMedical Research CouncilNational Institutes of HealthBiosensors International GroupCanada Research ChairsUniversity of BristolDepartment of Health and Social CareMedicines CompanyNational Institute for Health and Care ResearchEli Lilly and CompanyUniversity Hospitals Bristol NHS Foundation TrustNIHR Bristol Biomedical Research CentreNational Health and Medical Research CouncilAcademy of Medical SciencesCancer Research UKPatient-Centered Outcomes Research InstituteCHDI FoundationWellcome TrustAmgen
KeywordsSystematic reviewMedicineRandomized controlled trialPublication biasClinical trialMEDLINEComputer scienceMeta-analysisMedical physicsInternal medicine

Abstract

fetched live from OpenAlex

Assessment of risk of bias is regarded as an essential component of a systematic review on the effects of an intervention. The most commonly used tool for randomised trials is the Cochrane risk-of-bias tool. We updated the tool to respond to developments in understanding how bias arises in randomised trials, and to address user feedback on and limitations of the original tool.

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)0.326
metaresearch head score (Gemma)0.722
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.674
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3260.722
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0140.027
Bibliometrics0.0240.016
Science and technology studies0.0020.006
Scholarly communication0.0130.009
Open science0.0100.011
Research integrity0.0140.023
Insufficient payload (model declined to judge)0.0320.018

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 head0.856
GPT teacher head0.589
Teacher spread0.266 · 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 designNot applicable
DomainMethods
GenreMethods

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

Citations30,746
Published2019
Admission routes2
Has abstractyes

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