Systematic Review and Individual Patient Data Meta-Analysis of Sex Differences in Depression and Prognosis in Persons With Myocardial Infarction
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Bibliographic record
Abstract
OBJECTIVE: Using combined individual patient data from prospective studies, we explored sex differences in depression and prognosis post-myocardial infarction (MI) and determined whether disease indices could account for found differences. METHODS: Individual patient data analysis of 10,175 MI patients who completed diagnostic interviews or depression questionnaires from 16 prospective studies from the MINDMAPS study was conducted. Multilevel logistic and Cox regression models were used to determine sex differences in prevalence of depression and sex-specific effects of depression on subsequent outcomes. RESULTS: Combined interview and questionnaire data from observational studies showed that 36% (635/1760) of women and 29% (1575/5526) of men reported elevated levels of depression (age-adjusted odds ratio = 0.68, 95% confidence interval [CI] = 0.60-0.77). The risk for all-cause mortality associated with depression was higher in men (hazard ratio = 1.38, 95% CI = 1.30-1.47) than in women (hazard ratio = 1.22, 95% CI = 1.14-1.31; sex by depression interaction: p < .001). Low left ventricular ejection fraction (LVEF) was associated with higher depression scores in men only (sex by LVEF interaction: B = 0.294, 95% CI = 0.090-0.498), which attenuated the sex difference in the association between depression and prognosis. CONCLUSIONS: The prevalence of depression post-MI was higher in women than in men, but the association between depression and cardiac prognosis was worse for men. LVEF was associated with depression in men only and accounted for the increased risk of all-cause mortality in depressed men versus women, suggesting that depression in men post-MI may, in part, reflect cardiovascular disease severity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it