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Record W1988448166 · doi:10.3810/psm.2000.09.1204

Avoiding Repeat Cardiac Events

2000· article· en· W1988448166 on OpenAlexaff
Barry A. Franklin, Roy J. Shephard

Bibliographic record

VenueThe Physician and Sportsmedicine · 2000
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsSheridan College
Fundersnot available
KeywordsMedicineAspirinCoronary artery diseaseInternal medicineDiabetes mellitusCardiologySmoking cessationRisk factorBlood pressureDiseaseIntensive care medicineEndocrinologyPathology

Abstract

fetched live from OpenAlex

Multifactorial risk-factor modification-especially intensive ways to manage hyperlipidemia-may slow, halt, or even reverse the progression of coronary artery disease. The American Heart Association recently published comprehensive risk-reduction strategies in patients with coronary heart and vascular disease. These recommendations, endorsed by the American College of Cardiology, have been expanded and can be easily remembered as the ABCDESs of tertiary prevention: 'A': aspirin, alpha-tocopherol, and ACE inhibitors; 'B': beta-blockers, B-vitamins, and blood pressure control; 'C': cholesterol management; 'D': diabetes management and diet; 'E': exercise and estrogen therapy; and 'S': social support, smoking cessation, and stress management.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2000
Admission routes1
Has abstractyes

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