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GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy

2020· article· en· W3005727251 on OpenAlexaff
Holger J. Schünemann, Reem A. Mustafa, Jan Brożek, Karen R Steingart, Mariska Leeflang, M. Hassan Murad, Patrick M. Bossuyt, Paul Glasziou, Roman Jaeschke, Stefan Lange, Joerg J Meerpohl, Miranda Langendam, Monica Hultcrantz, Gunn Elisabeth Vist, Elie A. Akl, Mark Helfand, Nancy Santesso, Lotty Hooft, Rob Scholten, Måns Rosén, Anne WS Rutjes, Mark Crowther, Paola Muti, Heike Raatz, Mohammed T Ansari, John W Williams, Regina Kunz, Jeff Harris, Íngrid Arévalo Rodríguez, Mikashmi Kohli, Gordon Guyatt

Bibliographic record

VenueJournal of Clinical Epidemiology · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa Public HealthMcGill UniversityImpactMcMaster University
FundersEuropean Commission
KeywordsCertaintyTest (biology)GuidelineQuality of evidenceResearch designEvidence-based medicineSystematic reviewPsychologyEvidence-based practiceQuality (philosophy)MEDLINEMedicineMedical physicsApplied psychologyMeta-analysisMedical educationAlternative medicineStatisticsPathologyPolitical science

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)0.122
metaresearch head score (Gemma)0.528
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 score0.878
Threshold uncertainty score0.647

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1220.528
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0120.029
Bibliometrics0.0120.011
Science and technology studies0.0030.004
Scholarly communication0.0100.004
Open science0.0180.006
Research integrity0.0180.014
Insufficient payload (model declined to judge)0.0250.016

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 head0.988
GPT teacher head0.753
Teacher spread0.235 · 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

Citations383
Published2020
Admission routes1
Has abstractno

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