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Reporting of randomized factorial trials was frequently inadequate

2019· review· en· W2979201572 on OpenAlexafffund
Brennan C Kahan, Michael Tsui, Vipul Jairath, Anna Mae Scott, Douglas G. Altman, Elaine Beller, Diana Elbourne

Bibliographic record

VenueJournal of Clinical Epidemiology · 2019
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistryCancer Research UK
KeywordsFactorialFactorial experimentMedicineClinical trialRandomized controlled trialResearch designPsychological interventionFactorial analysisSample size determinationStatisticsInternal medicineMathematicsNursing

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)0.515
metaresearch head score (Gemma)0.881
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.485
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5150.881
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0130.018
Bibliometrics0.0180.014
Science and technology studies0.0030.006
Scholarly communication0.0110.012
Open science0.0050.008
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0120.002

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 head0.984
GPT teacher head0.764
Teacher spread0.220 · 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 designSystematic review
DomainReporting
GenreReview

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

Citations31
Published2019
Admission routes2
Has abstractno

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