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Record W1976355580 · doi:10.1007/s10115-011-0439-8

Finding best evidence for evidence-based best practice recommendations in health care: the initial decision support system design

2011· article· en· W1976355580 on OpenAlexaff
Nick Cercone, Xiangdong An, Jiye Li, Zhenmei Gu, Aijun An

Bibliographic record

VenueKnowledge and Information Systems · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsYork University
Fundersnot available
KeywordsBest practiceMedical prescriptionClinical decision support systemDecision support systemHarmHealth careQuality (philosophy)eHealthEvidence-based practiceMedicineRisk analysis (engineering)Computer scienceData miningNursingPsychologyAlternative medicine

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)0.330
metaresearch head score (Gemma)0.697
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.670
Threshold uncertainty score0.826

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3300.697
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0130.010
Bibliometrics0.0200.009
Science and technology studies0.0040.006
Scholarly communication0.0220.018
Open science0.0070.012
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0110.001

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 head0.540
GPT teacher head0.498
Teacher spread0.042 · 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 designSimulation or modeling
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

Citations8
Published2011
Admission routes1
Has abstractno

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