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Record W2059333369 · doi:10.1136/bmj.h870

Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients

2015· article· en· W2059333369 on OpenAlexaff
Alfonso Iorio, F.A. Spencer, Maicon Falavigna, Ana Carolina Alba, Eddy Lang, Bernard Burnand, Thomas McGinn, Jill A. Hayden, Katrina Williams, Bev Shea, Robert Wolff, T. Kujpers, Pablo Perel, Per Olav Vandvik, Paul Glasziou, Holger J. Schünemann, Gordon Guyatt

Bibliographic record

VenueBMJ · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsBruyèreOttawa HospitalUniversity of OttawaInstitute for Clinical Evaluative SciencesUniversity of CalgaryDalhousie UniversityUniversity Health NetworkToronto General HospitalAlberta Health ServicesMcMaster University
Fundersnot available
KeywordsConfidence intervalEvent (particle physics)StatisticsRating systemMedicinePsychologyMathematicsEconomics

Abstract

fetched live from OpenAlex

This paper provides guidance for the use of the GRADE approach to determine confidence

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.426
metaresearch head score (Gemma)0.759
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.574
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4260.759
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0210.039
Bibliometrics0.0310.018
Science and technology studies0.0030.007
Scholarly communication0.0100.010
Open science0.0100.010
Research integrity0.0090.018
Insufficient payload (model declined to judge)0.0090.002

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 head0.876
GPT teacher head0.611
Teacher spread0.265 · 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 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

Citations865
Published2015
Admission routes1
Has abstractyes

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