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GRADE Guidelines: 16. GRADE evidence to decision frameworks for tests in clinical practice and public health

2016· article· en· W2281906779 on OpenAlexaff
Holger J. Schünemann, Reem A. Mustafa, Jan Brożek, Nancy Santesso, Pablo Alonso‐Coello, Gordon Guyatt, Rob Scholten, Miranda Langendam, Mariska Leeflang, Elie A. Akl, Jasvinder A. Singh, Joerg J Meerpohl, Monica Hultcrantz, Patrick M. Bossuyt, Andrew D Oxman, Stefan Lange, Elena Parmelli, Jenny Moberg, Sarah Rosenbaum, Romina Brignardello‐Petersen, Wojtek Wiercioch, Marina Davoli, Artur Nowak, Bart Dietl

Bibliographic record

VenueJournal of Clinical Epidemiology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityCochrane
Fundersnot available
KeywordsGrading (engineering)Test (biology)GuidelineTransparency (behavior)Computer scienceEvidence-based medicineKnowledge managementManagement scienceProcess managementMedicineAlternative medicineEngineering

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)0.072
metaresearch head score (Gemma)0.415
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 score0.928
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.415
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0130.030
Bibliometrics0.0150.015
Science and technology studies0.0030.003
Scholarly communication0.0120.005
Open science0.0210.009
Research integrity0.0190.014
Insufficient payload (model declined to judge)0.0290.019

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 head0.955
GPT teacher head0.835
Teacher spread0.120 · 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

Citations340
Published2016
Admission routes1
Has abstractno

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