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Record W2129688566 · doi:10.1503/cmaj.090449

AGREE II: advancing guideline development, reporting and evaluation in health care

2010· review· en· W2129688566 on OpenAlexafffundvenue
Melissa Brouwers, Michelle E. Kho, George P. Browman, Jako Burgers, Françoise Cluzeau, Gene Feder, Béatrice Fervers, Ian D. Graham, Jeremy Grimshaw, Steven Hanna, Peter Littlejohns, Julie Makarski, Louise Zitzelsberger

Bibliographic record

VenueCanadian Medical Association Journal · 2010
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsCanadian Partnership Against CancerInstitute of Population and Public HealthBC Cancer AgencyOttawa HospitalMcMaster UniversityCancer Care OntarioCanadian Institutes of Health Research
FundersCanadian Institutes of Health Research
KeywordsGuidelineComputer scienceHealth careQuality (philosophy)Clinical PracticeMedicineProcess managementMedical educationManagement scienceNursingPathologyBusinessPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Clinical practice guidelines, which are systematically developed statements aimed at helping people make clinical, policy-related and system-related decisions,[1][1],[2][2] frequently vary widely in quality.[3][3],[4][4] A strategy was needed to differentiate among guidelines and ensure that those

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)0.323
metaresearch head score (Gemma)0.451
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.677
Threshold uncertainty score0.835

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3230.451
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0170.019
Science and technology studies0.0030.004
Scholarly communication0.0080.008
Open science0.0070.009
Research integrity0.0070.014
Insufficient payload (model declined to judge)0.0070.005

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 head0.208
GPT teacher head0.539
Teacher spread0.331 · 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
DomainEvaluation
GenreReview

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

Citations3,704
Published2010
Admission routes3
Has abstractyes

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