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Reproducible research practices are underused in systematic reviews of biomedical interventions

2017· review· en· W2766439930 on OpenAlexafffund
Matthew J. Page, Douglas G. Altman, Larissa Shamseer, Joanne E. McKenzie, Nadera Ahmadzai, Dianna Wolfe, Fatemeh Yazdi, Ferrán Catalá-López, Andrea C. Tricco, David Moher

Bibliographic record

VenueJournal of Clinical Epidemiology · 2017
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's HospitalOttawa HospitalUniversity of Ottawa
FundersNational Health and Medical Research CouncilGeneralitat ValencianaUniversity of BristolNational Institute for Health and Care ResearchCancer Research UKMedical Research CouncilUniversity of Ottawa
KeywordsSystematic reviewPsychological interventionMedicineMEDLINEData scienceManagement scienceComputer scienceEngineeringNursingPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.803
metaresearch head score (Gemma)0.952
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: Review · Consensus signal: Review
Teacher disagreement score0.197
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8030.952
Meta-epidemiology (narrow)0.0040.009
Meta-epidemiology (broad)0.0180.013
Bibliometrics0.0280.023
Science and technology studies0.0070.033
Scholarly communication0.0260.026
Open science0.0130.018
Research integrity0.0190.022
Insufficient payload (model declined to judge)0.0030.001

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 head0.998
GPT teacher head0.861
Teacher spread0.137 · 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
GenreReview

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

Citations117
Published2017
Admission routes2
Has abstractno

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