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Record W2037298142 · doi:10.3810/psm.2000.10.1240

Reducing the Risk of Heart Disease and Stroke

2000· article· en· W2037298142 on OpenAlexaff
Barry A. Franklin, Wendy Sanders

Bibliographic record

VenueThe Physician and Sportsmedicine · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsMedicineStroke (engine)Coronary artery diseaseDiabetes mellitusCardiologyInternal medicineDiseaseRisk factorCause of deathVascular diseaseRevascularizationMyocardial infarctionIntensive care medicine

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States, responsible for almost 50% of all deaths. Compelling scientific evidence, including data from recent studies in patients with coronary artery disease (CAD), demonstrates that comprehensive risk factor interventions-including regular physical activity-augment physical work capacity, increase overall survival, improve quality of life, decrease the need for coronary revascularization procedures, and reduce the incidence of subsequent cardiovascular events ((1)). The rationale for this approach extends to patients with other documented atherosclerotic disease, (eg, transient ischemic attack, stroke ((2)), or aortic or peripheral vascular disease) because CAD is a leading cause of death and disability in these patient subsets. Not only do stroke and CAD share common risk factors (eg, hypertension, hypercholesterolemia, and diabetes mellitus), but 32% to 65% of stroke patients have CAD ((3)).

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.009
GPT teacher head0.283
Teacher spread0.275 · 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
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

Citations13
Published2000
Admission routes1
Has abstractyes

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