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Methods proposed for monitoring the implementation of evidence-based research: a cross-sectional study

2024· article· en· W4390618455 on OpenAlexaff
Livia Puljak, Małgorzata M Bała, J. Zając, Tomislav Meštrović, Sandra C. Buttiġieġ, Mary Yanakoulia, Matthias Briel, Carole Lunny, Wiktoria Leśniak, Tina Poklepović Peričić, Pablo Alonso‐Coello, Mike Clarke, Benjamin Djulbegović, Gerald Gartlehner, Konstantinos Giannakou, Anne‐Marie Glenny, Claire Glenton, Gordon Guyatt, Lars G. Hemkens, John P. A. Ioannidis, Roman Jaeschke, Karsten Juhl Jørgensen, Carolina Castro Martins, Ana Marušić, Lawrence Mbuagbaw, Jóse Francisco Meneses Echavez, David Moher, Barbara Nußbaumer-Streit, Matthew J. Page, Giordano Pérez‐Gaxiola, Karen A. Robinson, Georgia Salanti, Ian J. Saldanha, Jelena Savović, James Thomas, Andrea C. Tricco, Peter Tugwell, Joost van Hoof, Dawid Pieper

Bibliographic record

VenueJournal of Clinical Epidemiology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoOttawa HospitalUniversity of OttawaQueen's UniversityMcMaster UniversityImpactPublic Health OntarioSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaCochraneSt. Michael's Hospital
FundersEuropean Commission
KeywordsCross-sectional studyEnvironmental healthMedicinePathology

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)0.234
metaresearch head score (Gemma)0.267
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2340.267
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0090.007
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.993
GPT teacher head0.900
Teacher spread0.093 · 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
DomainMethods
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

Citations5
Published2024
Admission routes1
Has abstractno

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