Relative associations between depression and anxiety on adverse cardiovascular events: does a history of coronary artery disease matter? A prospective observational study
Bibliographic record
Abstract
OBJECTIVES: To assess whether depression and anxiety increase the risk of mortality and major adverse cardiovascular events (MACE), among patients with and without coronary artery disease (CAD). DESIGN AND SETTING, AND PATIENTS: DECADE (Depression Effects on Coronary Artery Disease Events) is a prospective observational study of 2390 patients referred at the Montreal Heart Institute. Patients were followed for 8.8 years, between 1998 and 2009. Depression and anxiety were assessed using a psychiatric interview (Primary Care Evaluation of Mental Disorders, PRIME-MD). Outcomes data were obtained from Quebec provincial databases. MAIN OUTCOME MEASURES: All-cause mortality and MACE. RESULTS: After adjustment for covariates, patients with depression were at increased risks of all-cause mortality (relative risk (RR)=2.84; 95% CI 1.25 to 6.49) compared with patients without depression. Anxiety was not associated with increased mortality risks (RR=0.86; 95% CI 0.31 to 2.36). When patients were stratified according to CAD status, depression increased the risk of mortality among patients with no CAD (RR=4.39; 95% CI 1.12 to 17.21), but not among patients with CAD (RR=2.32; 95% CI 0.78 to 6.88). Neither depression nor anxiety was associated with MACE among patients with or without CAD. CONCLUSIONS AND RELEVANCE: Depression, but not anxiety, was an independent risk factor for all-cause mortality in patients without CAD. The present study contributes to a better understanding of the relative and unique role of depression versus anxiety among patients with versus without CAD.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".