Long-Term Adverse Outcomes in Black Women With Ischemia and No Obstructive Coronary Artery Disease: A Study of the WISE (Women’s Ischemia Syndrome Evaluation) Cohort
Bibliographic record
Abstract
ardiovascular disease remains the leading cause of death for women in the United States, with substantial racial and ethnic disparities.1,2 Compared with their White counterparts, cardiovascular health for Black women is differentiated by a heavier burden of traditional risk factors, such as diabetes and hypertension, earlier development of cardiovascular disease, and nearly 20% higher rates of cardiovascular mortality.1,2 Over the past 2 decades, the WISE cohort studies (Women's Ischemia Syndrome Evaluation) have provided data on pathophysiology for women with ischemic heart disease.Two-thirds of the original WISE cohort had symptoms or signs of myocardial ischemia with no obstructive coronary artery disease (INOCA).These women have unfavorable long-term prognoses, with elevated rates of adverse events and mortality at 5 and 10 years compared with case-matched asymptomatic women.3,4 Little is known about the role of race and ethnicity in this prognosis.We characterized the risk profile for Black women with INOCA and explored the factors associated with long-term adverse outcomes in this population.The original WISE cohort recruited eligible women 18 years of age or older with symptoms or signs of myocardial ischemia undergoing clinically indicated coronary angiography from September 1996 to March 2000 with telephone follow-up obtained through March
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".