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Fragility index analysis for robustness of evidence in randomized controlled trials leading to the US Food and Drug Administration approval of pharmacological therapies for endometriosis

2025· article· en· W4415496638 on OpenAlexaff
Yael Yagur, Nir Horesh, Gabriel Levin, Raanan Meyer

Bibliographic record

VenueFertility and Sterility · 2025
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsFragilityFood and drug administrationRandomized controlled trialEndometriosisRobustness (evolution)Drug

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)0.502
metaresearch head score (Gemma)0.805
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5020.805
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0170.034
Bibliometrics0.0140.009
Science and technology studies0.0020.010
Scholarly communication0.0080.009
Open science0.0060.008
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0230.001

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 head0.620
GPT teacher head0.568
Teacher spread0.052 · 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 designMeta-analysis
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
Published2025
Admission routes1
Has abstractno

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