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Record W1980472079 · doi:10.1136/bmj.c117

Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses

2010· article· en· W1980472079 on OpenAlexaff
Xin Sun, Matthias Briel, Stephen D. Walter, Gordon Guyatt

Bibliographic record

VenueBMJ · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSubgroup analysisCredibilityPsychologyComputer scienceInformation retrievalMathematicsStatisticsPolitical science

Abstract

fetched live from OpenAlex

How can we tell the difference between spurious and real subgroup effects? This article identifies new criteria and proposes a checklist for judging the credibility of subgroup analyses

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)0.764
metaresearch head score (Gemma)0.952
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.236
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7640.952
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0100.012
Bibliometrics0.0180.009
Science and technology studies0.0070.022
Scholarly communication0.0140.020
Open science0.0130.015
Research integrity0.0160.028
Insufficient payload (model declined to judge)0.0040.001

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 head0.524
GPT teacher head0.588
Teacher spread0.064 · 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 designTheoretical or conceptual
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

Citations812
Published2010
Admission routes1
Has abstractyes

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