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Record W2259348130 · doi:10.1371/journal.pmed.1001950

Sharing Clinical Trial Data: A Proposal from the International Committee of Medical Journal Editors

2016· editorial· en· W2259348130 on OpenAlexafffund
Darren B. Taichman, Joyce Backus, Christopher Baethge, Howard Bauchner, Peter W. de Leeuw, Jeffrey M. Drazen, John Fletcher, Frank Frizelle, Trish Groves, Abraham Haileamlak, Astrid James, Christine Lainé, Larry Peiperl, Anja Pinborg, Peush ‎Sahni, Sinan Wu

Bibliographic record

VenuePLoS Medicine · 2016
Typeeditorial
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsCanadian Medical Association
FundersU.S. National Library of MedicineCanadian Medical Association
KeywordsMedical journalClinical trialData sharingMedicineMEDLINEAlternative medicineFamily medicineMedical researchPublication biasMedical educationMedical physicsMeta-analysisPolitical sciencePathologyLaw

Abstract

fetched live from OpenAlex

DOAJ is a unique and extensive index of diverse open access journals from around the world, driven by a growing community, committed to ensuring quality content is freely available online for everyone.

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)0.124
metaresearch head score (Gemma)0.274
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.992
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1240.274
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0050.003
Science and technology studies0.0080.008
Scholarly communication0.0210.012
Open science0.0080.006
Research integrity0.0520.084
Insufficient payload (model declined to judge)0.0070.008

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 head0.578
GPT teacher head0.626
Teacher spread0.048 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainReproducibility
GenreEditorial

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

Citations110
Published2016
Admission routes2
Has abstractyes

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