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Record W2006831410 · doi:10.1371/journal.pmed.1001149

Disclosure of Investigators' Recruitment Performance in Multicenter Clinical Trials: A Further Step for Research Transparency

2011· article· en· W2006831410 on OpenAlexafffund
Rafael Dal‐Ré, David Moher, Christian Gluud, Shaun Treweek, Jacques Demotes‐Mainard, Xavier Carné

Bibliographic record

VenuePLoS Medicine · 2011
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersAgencia Española de Medicamentos y Productos SanitariosUniversity of OttawaGlaxoSmithKline
KeywordsClinical trialTransparency (behavior)Trial registrationMedicineClinical researchMedical physicsFamily medicineInternal medicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Rafael Dal-Ré and colleagues argue that the recruitment targets and performance of all site investigators in multi-centre clinical trials should be disclosed in trial registration sites before a trial starts, and when it ends.

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)0.673
metaresearch head score (Gemma)0.770
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.327
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6730.770
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.004
Science and technology studies0.0060.021
Scholarly communication0.0220.029
Open science0.0080.014
Research integrity0.0500.057
Insufficient payload (model declined to judge)0.0040.002

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 head0.972
GPT teacher head0.727
Teacher spread0.244 · 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
DomainReproducibility
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

Citations18
Published2011
Admission routes2
Has abstractyes

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