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Record W1998298719 · doi:10.1016/j.jacc.2013.01.044

Risk Factor Control for Coronary Artery Disease Secondary Prevention in Large Randomized Trials

2013· article· en· W1998298719 on OpenAlexafffund
Michael E. Farkouh, William E. Boden, Vera Bittner, Victoria Muratov, Pamela Hartigan, May Ogdie, Marnie Bertolet, Shiny Mathewkutty, Koon Teo, David J. Maron, Sanjum S. Sethi, Michaël Domanski, Robert L. Frye, Valentı́n Fuster

Bibliographic record

VenueJournal of the American College of Cardiology · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsMcMaster University Medical CentreHeart and Stroke FoundationUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNovartis Pharmaceuticals CorporationGlaxoSmithKlineGE HealthcareAstellas Pharma USPfizerEli Lilly and CompanySanofiMerckAbbott Laboratories
KeywordsMedicineCoronary artery diseaseInternal medicineDiabetes mellitusGlycemicGuidelineRandomized controlled trialBlood pressureRisk factorRevascularizationClinical trialCardiologySurgeryMyocardial infarctionEndocrinology

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)0.039
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.058
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0110.013
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.333
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations163
Published2013
Admission routes2
Has abstractno

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