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Delphi consensus on core criteria set selecting among health-related outcome measures (HROM) in primary health care

2020· article· en· W3024509053 on OpenAlexafffund
Pasqualina Santaguida, Douglas Oliver, Anne Gilsing, Larkin Lamarche, Lauren E. Griffith, Julie Richardson, Monica Kastner, Parminder Raina, Lisa Dolovich

Bibliographic record

VenueJournal of Clinical Epidemiology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of TorontoMcMaster UniversityImpact
FundersHealth CanadaGovernment of OntarioMcMaster University
KeywordsDelphi methodOutcome (game theory)Core (optical fiber)DelphiSet (abstract data type)MedicineHealth careMEDLINEPrimary careConsensus conferenceFamily medicineComputer sciencePolitical scienceStatisticsMathematics

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)0.259
metaresearch head score (Gemma)0.295
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2590.295
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.004
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0020.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.762
GPT teacher head0.656
Teacher spread0.105 · 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 designQualitative
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

Citations8
Published2020
Admission routes2
Has abstractno

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