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Improving grading of recommendations assessment, development, and evaluation evidence tables part 4: a three-arm noninferiority randomized trial demonstrates improved understanding of content in summary of findings tables with a new format

2022· article· en· W4310894423 on OpenAlexaff
Rebecca L. Morgan, Juan José Yepes-Núñez, Joycelyne Ewusie, Lawrence Mbuagbaw, Stephanie Chang, Tejan Baldeh, Susanne Hempel, Mark Helfand, Paul Shekelle, Timothy J Wilt, Holger J. Schünemann

Bibliographic record

VenueJournal of Clinical Epidemiology · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityImpact
Fundersnot available
KeywordsRandomized controlled trialGrading (engineering)MedicineChecklistGuidelineSystematic reviewMedical physicsMEDLINEMedical educationPsychologySurgeryEngineering

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)0.202
metaresearch head score (Gemma)0.645
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2020.645
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0130.022
Bibliometrics0.0180.012
Science and technology studies0.0020.002
Scholarly communication0.0100.007
Open science0.0060.006
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0220.003

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 head0.904
GPT teacher head0.615
Teacher spread0.289 · 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 designRandomized trial
DomainReporting
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

Citations10
Published2022
Admission routes1
Has abstractno

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