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Record W3208185003 · doi:10.1371/journal.pmed.1003844

Medical journal requirements for clinical trial data sharing: Ripe for improvement

2021· article· en· W3208185003 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLoS Medicine · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersU.S. Food and Drug AdministrationSvenska LäkaresällskapetUppsala UniversitetAgence Nationale de la RechercheLaura and John Arnold Foundation
KeywordsClinical trialData sharingMedicineMedical researchMEDLINEMedical physicsComputer scienceIntensive care medicineAlternative medicineInternal medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

In some science, technology, engineering, and mathematics (STEM) fields, data sharing is the norm (e.g., physics or space science). However, this is currently not the case in biomedicine, except for certain exceptions in areas such as genomics. For therapeutic research, data sharing is expected to maximize the value of research for clinical practice by means of greater transparency and opportunities for external researchers to reanalyze, synthesize, replicate, and build upon previous evidence. Examples include reanalyses, secondary analyses, individual patient data (IPD) meta-analyses, and methodological evaluations. Maximizing the efficient use of clinical research data is important in the development of new therapeutic options, including treatments for the Coronavirus Disease 2019 (COVID-19).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.053
metaresearch head score (Gemma)0.158
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.511
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0530.158
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0050.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.819
GPT teacher head0.604
Teacher spread0.215 · 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