Guideline-directed medical therapy for secondary prevention after coronary artery bypass grafting in patients with depression
Bibliographic record
Abstract
BACKGROUND: We hypothesized that depressed patients would have lower use of guideline-directed medical therapy for secondary prevention of cardiovascular events following coronary artery bypass grafting (CABG). METHODS: We included all patients who underwent primary isolated CABG in Sweden between 2006 and 2008. We cross-linked individual level data from national Swedish registers. Preoperative depression was defined as at least one antidepressant prescription dispensed before surgery. We defined medication use as at least two dispensed prescriptions in each medication class (antiplatelet agents, beta-blockers, angiotensin-converting enzyme inhibitors (ACEI)/angiotensin II receptor blocker (ARB), and statins) within a rolling 12 month period. We calculated adjusted risk ratios (RR) for the use of each medication class, and for all four classes, after one and four years, respectively. RESULTS: During the first year after CABG, 93% of all patients (n = 10,586) had at least two dispensed prescriptions for an antiplatelet agent, 68% for an ACEI/ARB, 91% for a beta-blocker, and 92% for a statin. 57% had prescriptions for all four medication classes. After four years (n = 4034), 44% had filled prescriptions for all four medication classes. Preoperative depression was not significantly associated with a lower use of all four medication classes after one year (RR 0.98, 95% confidence interval (CI) 0.93-1.03) or after four years (RR 0.97, 95% CI 0.86-1.09). CONCLUSIONS: Preoperative depression was not associated with lower use of guideline-directed medical therapy for secondary prevention after CABG. These findings suggest that the observed higher mortality following CABG among depressed patients is not explained by inadequate secondary prevention medication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".