Abstract 10987: Acute Coronary Syndrome, Depression, and Anxiety in Female Patients
Bibliographic record
Abstract
Introduction: Female patients are significantly more likely than male patients to experience symptoms of depression and anxiety post-acute coronary syndrome (ACS), correlated with higher rates of cardiovascular morbidity and mortality. Yet, it is unclear if all female patients are impacted broadly or if specific subgroups of female patients are at elevated risk. We aimed to identify the cardiovascular and psychosocial variables correlated with increased depression and anxiety symptoms immediately post-ACS as well as at 3 and 6-month follow-up. Hypothesis: There is a combination of cardiovascular and psychosocial factors associated with elevated depressive/anxious symptoms (Hospital Anxiety and Depression Scale (HADS) score ≥8 on the depression/anxiety subscales) in female patients post-ACS. Methods: This was a prospective multi-center questionnaire-based clinical research study featuring data from 6 sites across Canada using a logistic regression model to delineate multivariate strength of association. Baseline visit (within 72 hours of ACS) included HADS and a sociodemographic questionnaire. Follow-up visits (3 and 6-months) include HADS, Cardiac Anxiety Questionnaire, new health events, mortality, Short Form-12 Health Survey, and Somatic Symptom Scale-8. Results: A total of 245 patients were included in analysis (Table 1). HADS-A≥8 was associated with increased health anxiety at baseline (OR6.56; p<0.001). Conversely, HADS-D≥8 was associated with low social support at baseline (OR3.35; p=0.005) and 3-month follow-up (OR22.65; p<0.001) as well as previous diagnosis of depression at 3-month (OR22.73; p<0.001) and 6-month (OR5.50; p=0.02) follow-up. Conclusion: We identify key factors associated with symptoms of depression and anxiety post-ACS in female patients. Developing a deeper understanding of the relationship between mental health and ACS will benefit patients by informing intervention and prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".