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GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes

2012· review· en· W2107021177 on OpenAlexaff
Gordon Guyatt, Andrew D Oxman, Shahnaz Sultan, Jan Brożek, Paul Glasziou, Pablo Alonso‐Coello, David C. Atkins, Regina Kunz, Víctor M. Montori, Roman Jaeschke, David M. Rind, Philipp Dahm, Elie A. Akl, Joerg J Meerpohl, Gunn Elisabeth Vist, Elise Berliner, Susan L. Norris, Yngve Falck–Ytter, Holger J. Schünemann

Bibliographic record

VenueJournal of Clinical Epidemiology · 2012
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConfidence intervalOutcome (game theory)Rating scaleGuidelineRating systemOrdinal ScaleMedicineStatisticsMathematics

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)0.044
metaresearch head score (Gemma)0.199
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 score0.956
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.199
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0080.019
Bibliometrics0.0090.007
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0140.006
Research integrity0.0140.009
Insufficient payload (model declined to judge)0.0340.025

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 head0.943
GPT teacher head0.720
Teacher spread0.223 · 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

Citations745
Published2012
Admission routes1
Has abstractno

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