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GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

2020· article· en· W3087770431 on OpenAlexafffund
Jan Brożek, Carlos Canelo‐Aybar, Elie A. Akl, James M. Bowen, John R. Bucher, Weihsueh A. Chiu, M Cronin, Benjamin Djulbegović, Maicon Falavigna, Gordon Guyatt, Ami A. Gordon, Michele Hilton Boon, Raymond Hutubessy, Manuela Joore, Srinivasa Vittal Katikireddi, Judy S. LaKind, Miranda Langendam, Veena Manja, Kristen Magnuson, Alexander G. Mathioudakis, Joerg J Meerpohl, Dominik Mertz, Roman Mezencev, Rebecca L. Morgan, Gian Paolo Morgano, Reem A. Mustafa, Martín O’Flaherty, Grace Patlewicz, John J. Riva, Margarita Posso, Andrew A. Rooney, Paul M. Schlosser, Lisa Schwartz, Ian Shemilt, Jean‐Éric Tarride, Kristina A. Thayer, Katya Tsaioun, Luke Vale, John F. Wambaugh, Jessica Wignall, Ashley R. Williams, Feng Xie, Yuan Zhang, Holger J. Schünemann

Bibliographic record

VenueJournal of Clinical Epidemiology · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster University Medical CentreHamilton Health SciencesMcMaster UniversityImpactToronto Public HealthCochrane
FundersMedical Research CouncilNational Institutes of HealthLiverpool John Moores UniversityPublic Health AgencyNational Institute of Environmental Health SciencesUniversity of GlasgowNational Institute for Health and Care ResearchUniversiteit van AmsterdamAmerican University of BeirutPublic Health Agency of CanadaWorld Health OrganizationUniversity of South FloridaU.S. Environmental Protection AgencyMcMaster UniversityUniversiteit Maastricht
KeywordsCertaintyTerminologyContext (archaeology)Multidisciplinary approachGrading (engineering)Health careManagement scienceConceptual modelSystematic reviewComputer scienceMedicineMEDLINEEngineeringMathematics

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)0.155
metaresearch head score (Gemma)0.496
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 score0.845
Threshold uncertainty score0.821

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1550.496
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0220.031
Bibliometrics0.0220.013
Science and technology studies0.0020.004
Scholarly communication0.0110.005
Open science0.0180.007
Research integrity0.0150.017
Insufficient payload (model declined to judge)0.0120.005

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 head0.948
GPT teacher head0.695
Teacher spread0.253 · 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

Citations289
Published2020
Admission routes2
Has abstractno

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