Abstract P007: Sex-Specific Disparities in Risk Factor Control of Patients Undergoing Elective Percutaneous Coronary or Peripheral Intervention
Bibliographic record
Abstract
Introduction: The American Heart Association (AHA) developed 7 health metrics to define “ideal cardiovascular health” in its 2020 Impact Goal. Sex-specific disparities in attainment of the 7 health metrics in patients undergoing elective percutaneous or peripheral interventions has not been well characterized. Methods: We interviewed 1,517 patients (1,127 males and 390 females) undergoing elective percutaneous coronary or peripheral intervention at a large tertiary care center between November 2010 and March 2015. Survey data was used to reconstruct the 7 health metrics (blood pressure, physical activity, cholesterol, diet, weight, smoking status, and metabolic control). Multivariable linear regression was performed to identify characteristics associated with ideal metric attainment in the overall cohort and when stratified by sex. Results: Overall, males were younger, less likely to be white, more likely to be married, and had higher levels of education and higher prevalence of prior coronary artery disease than females (p<.05 for each). Males achieved fewer ideal health metrics than females (2.0 ± 1.2 vs 2.3 ± 1.1, p<.01), including poorer attainment of the ideal smoking (p<.01), physical activity (p<.01), and diet health metrics (p<.05). Females had poorer attainment of the ideal weight (p<.05) and cholesterol health metrics (p=.01). After multivariable adjustment, males achieved fewer ideal health metrics than females (p<.01; Table). Sex-specific differences are presented in the Table, which includes single marital status and depression as negative predictors of ideal metric attainment in males, and a reduced ejection fraction as a negative predictor in females. Conclusions: Attainment of the 7 AHA ideal health metrics is low in both males and females undergoing elective percutaneous coronary or peripheral intervention. Sex-specific disparities in risk factor control illustrate interesting areas for further exploration into the predictors of behavior that may guide future targeted interventions.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".