Social Support, Depression, and Mortality During the First Year After Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: We previously reported that depression after myocardial infarction (MI) increases the long-term risk of cardiac mortality. Other research suggests that social support may also influence prognosis. This article examines the interrelationships between baseline depression and social support in terms of cardiac prognosis and changes in depression symptoms over the first post-MI year. METHODS AND RESULTS: For this study, 887 patients completed the Beck Depression Inventory (BDI) and the Perceived Social Support Scale (PSSS) at about 7 days after MI. Some 32% had BDIs > or =10, indicating mild to moderate depression. One-year survival status was determined for all patients. Follow-up interviews, including the BDI, were conducted with 89% of survivors. There were 39 deaths (35 cardiac). Elevated BDI scores were related to cardiac mortality (P=0.0006), but PSSS scores and other measures of social support were not. There was a significant interaction between depression and the PSSS (P=0. 016). The relationship between depression and cardiac mortality decreased with increasing support. Furthermore, residual change score analysis revealed that among 1-year survivors who had been depressed at baseline, higher baseline social support was related to more improvement in depression symptoms than expected. CONCLUSIONS: Post-MI depression is a predictor of 1-year cardiac mortality, but social support is not directly related to survival. However, very high levels of support appear to buffer the impact of depression on mortality. Furthermore, high levels of support predict improvements in depression symptoms over the first post-MI year in depressed patients. High levels of support may protect patients from the negative prognostic consequences of depression because of improvements in depression symptoms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".