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Record W2793971788 · doi:10.1136/bmj.k959

Trials that say “maybe”: the disconnect between exploratory and confirmatory testing after drug approval

2018· article· en· W2793971788 on OpenAlexafffund
Benjamin Gregory Carlisle, Carole A. Federico, Jonathan Kimmelman

Bibliographic record

VenueBMJ · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsHarmClinical trialRepurposingExploratory researchPsychologyExploratory analysisMedicineSocial psychologyComputer scienceData scienceSociologySocial scienceInternal medicineEngineering

Abstract

fetched live from OpenAlex

Clinical trials that explore the repurposing of drugs for off-label uses are common. But without a commitment to rigorously testing the hypotheses generated by these exploratory trials, patients are put in harm’s way, argue Benjamin Carlisle and colleagues

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)0.548
metaresearch head score (Gemma)0.764
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.452
Threshold uncertainty score0.557

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5480.764
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0030.003
Science and technology studies0.0060.050
Scholarly communication0.0180.029
Open science0.0080.008
Research integrity0.0460.060
Insufficient payload (model declined to judge)0.0080.006

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 head0.306
GPT teacher head0.441
Teacher spread0.135 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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