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Record W1998990594 · doi:10.1503/cmaj.1041669

Outcome reporting bias in government-funded RCTs

2005· letter· en· W1998990594 on OpenAlexvenueaboutno aff
Lorenzo Moja

Bibliographic record

VenueCanadian Medical Association Journal · 2005
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialProtocol (science)Government (linguistics)Outcome (game theory)MedicineFamily medicineAlternative medicineMEDLINEPolitical scienceSurgery

Abstract

fetched live from OpenAlex

An-Wen Chan and associates,[1][1] in their evaluation of outcome reporting bias in 48 randomized controlled trials funded by the Canadian Institutes of Health Research (CIHR), found that a high number (median 26) of outcomes were declared in each protocol, but not all of these outcomes were reported

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)0.434
metaresearch head score (Gemma)0.822
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.698

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4340.822
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0070.007
Science and technology studies0.0040.017
Scholarly communication0.0100.012
Open science0.0070.007
Research integrity0.0550.042
Insufficient payload (model declined to judge)0.0060.004

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 head0.654
GPT teacher head0.487
Teacher spread0.167 · 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
DomainReporting
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

Citations4
Published2005
Admission routes2
Has abstractyes

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