Depression and self-reported functional status: impact on mortality following acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVE: The cause of increased post-AMI (acute myocardial infarction) mortality associated with depression remains poorly elucidated. The objective of this study was to examine the extent to which self-reported cardiac functional status accounted for depression-mortality associations following AMI. METHODS: Using a prospective cohort design (n = 1941), the authors obtained self-reported measures of depression and developed profiles of the patients' pre-hospitalization cardiac risks, co-morbid conditions and drugs and revascularization procedures during or following index AMI hospitalization. To create these profiles, the patients' self-reports were retrospectively linked to no less than 12 years' worth of previous hospitalization data. Mortality rates 2 years after acute MI were examined with and without sequential risk adjustment for age, sex, income, cardiovascular risk, co-morbid conditions, selected process-of-care factors and self-reported cardiac functional status. RESULTS: Depression was strongly correlated with 2-year mortality rate [crude hazard ratio (HR) of severe vs. minimal depression category, 2.48 (95% CI 1.20-5.15); P = 0.01]. However, after sequential adjustment for age, sex, income and self-reported cardiac functional status, the effect of depression was greatly attenuated [adjusted HR for severe vs. minimal depression category, 1.35 (95% CI 0.63-2.87); P = 0.44]. Cardiac risk factors and non-cardiac co-morbidities had negligible explanatory effect. DISCUSSION: The main factor determining the increased mortality rate in depressed patients is self-reported cardiac functional status. Efforts to address increased mortality in depressed patients with cardiovascular illnesses should focus on processes that impact cardiac functional status.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".