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Record W4361828956 · doi:10.2139/ssrn.4401556

Operationalizing the GRADE-Equity Criterion to Inform Guideline Recommendations: Application to a Medical Cannabis Guideline

2023· preprint· en· W4361828956 on OpenAlexaff
Omar Dewidar, Jordi Pardo Pardo, Vivian Welch, Glen Hazlewood, Andrea Darzi, Cheryl Barnabé, Kevin Pottie, Jennifer Petkovic, Shawn Kuria, Samuel Sha, Sarah Allam, Jason W. Busse, Gordon Guyatt, Holger J. Schünemann, Peter Tugwell

Bibliographic record

VenueSSRN Electronic Journal · 2023
Typepreprint
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsWestern UniversityMcMaster UniversityImpactUniversity of CalgaryÉlisabeth Bruyère HospitalUniversity of TorontoBruyèreUniversity of Ottawa
Fundersnot available
KeywordsGuidelineOperationalizationEquity (law)CannabisBusinessMedicineActuarial scienceAccountingPolitical sciencePsychiatry

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)0.098
metaresearch head score (Gemma)0.479
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.479
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.006
Science and technology studies0.0020.002
Scholarly communication0.0080.006
Open science0.0020.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.134
GPT teacher head0.496
Teacher spread0.361 · 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 designTheoretical or conceptual
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

Citations2
Published2023
Admission routes1
Has abstractno

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