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GRADE equity guidelines 2: considering health equity in GRADE guideline development: equity extension of the guideline development checklist

2017· article· en· W2613538222 on OpenAlexaff
Elie A. Akl, Vivian Welch, Kevin Pottie, Javier Eslava‐Schmalbach, Andrea Darzi, Iván Solà, Srinivasa Vittal Katikireddi, Jasvinder A. Singh, M. Hassan Murad, Joerg J Meerpohl, Roger Stanev, Eddy Lang, Elizabeth Matovinovic, Beverley Shea, Thomas Agoritsas, Paul Alexander, Alexandra Snellman, Romina Brignardello‐Petersen, David Gloss, Lehana Thabane, Chunhu Shi, Aírton Tetelbom Stein, Ravi Sharaf, Matthias Briel, Gordon Guyatt, Holger J. Schünemann, Peter Tugwell

Bibliographic record

VenueJournal of Clinical Epidemiology · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityOttawa HospitalImpactUniversity of CalgaryHamilton Health SciencesInstitute of Population and Public HealthBruyèreUniversity of Ottawa
FundersMedical Research Council
KeywordsGuidelineChecklistEquity (law)Psychological interventionActuarial scienceBusinessMedicineAccountingPolitical sciencePsychologyNursing

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)0.183
metaresearch head score (Gemma)0.527
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1830.527
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0070.004
Science and technology studies0.0040.003
Scholarly communication0.0080.007
Open science0.0060.012
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0050.002

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 head0.816
GPT teacher head0.690
Teacher spread0.127 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations75
Published2017
Admission routes1
Has abstractno

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