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Record W2470058069 · doi:10.1136/bmj.i2016

GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: Introduction

2016· article· en· W2470058069 on OpenAlexaff
Pablo Alonso‐Coello, Holger J. Schünemann, Jenny Moberg, Romina Brignardello‐Petersen, Elie A. Akl, Marina Davoli, Shaun Treweek, Reem A. Mustafa, Gabriel Rada, Sarah Rosenbaum, Angela Morelli, Gordon Guyatt, Andrew D Oxman

Bibliographic record

VenueBMJ · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCertaintyHealth careQuality (philosophy)Management scienceEvidence-based medicineClinical decision makingDecision analysisComputer scienceDecision qualityActuarial scienceRisk analysis (engineering)MedicineKnowledge managementBusinessAlternative medicineEconomicsFamily medicine

Abstract

fetched live from OpenAlex

Funding: Work on this article has been partially funded by the European Commission FP7 Program (grant agreement 258583) as part of the DECIDE project. Sole responsibility lies with the authors; the European Commission is not responsible for any use that may be made of the information contained therein.

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)0.343
metaresearch head score (Gemma)0.708
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.657
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3430.708
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0120.014
Bibliometrics0.0300.018
Science and technology studies0.0030.006
Scholarly communication0.0210.014
Open science0.0180.019
Research integrity0.0210.018
Insufficient payload (model declined to judge)0.0290.011

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 head0.418
GPT teacher head0.471
Teacher spread0.053 · 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 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

Citations1,310
Published2016
Admission routes1
Has abstractyes

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