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Record W1985850860 · doi:10.1503/cmaj.109-4980

IOM suggests ways to share clinical trial data

2015· article· en· W1985850860 on OpenAlexvenueno aff
Dane Wanniarachige

Bibliographic record

VenueCanadian Medical Association Journal · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsClinical trialData sharingAlternative medicineComputer scienceData scienceMedicineWorld Wide WebPathology

Abstract

fetched live from OpenAlex

In a bid to address long-standing concerns about accessing clinical trial data, the US Institute of Medicine (IOM) has released a 249-page report outlining strategies for sharing this information responsibly. “We think that the question today is not whether you share clinical trial data, but

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)0.500
metaresearch head score (Gemma)0.697
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.500
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5000.697
Meta-epidemiology (narrow)0.0020.005
Meta-epidemiology (broad)0.0030.010
Bibliometrics0.0150.013
Science and technology studies0.0080.020
Scholarly communication0.0330.056
Open science0.0100.034
Research integrity0.0440.052
Insufficient payload (model declined to judge)0.0310.021

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 head0.879
GPT teacher head0.588
Teacher spread0.292 · 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 designNot applicable
DomainReproducibility
GenreCommentary

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
Published2015
Admission routes1
Has abstractyes

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