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Record W4225639596 · doi:10.2139/ssrn.4077740

Subgroup Analyses in Randomised Controlled Trials Frequently Categorise Continuous Subgroup Information

2022· article· en· W4225639596 on OpenAlexaff
S. Faye Williamson, Michael J. Grayling, Adrian Mander, Nurulamin M Noor, Joshua Savage, Christina Yap, James Wason

Bibliographic record

VenueSSRN Electronic Journal · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsSubgroup analysisContinuous variableCategorical variableMedicineSample size determinationRandomized controlled trialStatisticsMathematicsInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.307
metaresearch head score (Gemma)0.724
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 score0.693
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3070.724
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0150.020
Bibliometrics0.0120.012
Science and technology studies0.0020.003
Scholarly communication0.0070.008
Open science0.0040.007
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0110.002

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 head0.297
GPT teacher head0.506
Teacher spread0.209 · 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

Citations0
Published2022
Admission routes1
Has abstractno

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