Bibliographic record
Abstract
Background: Depressive symptoms and major depressive disorder (MDD) occur ≥ 3 times as common in coronary artery disease (CAD) patients as in the general community, which confers an adjusted relative risk of 2 to 4 for mortality. There are emerging data on how to manage depressed CAD patients with MDD. Method: The two previous clinical trials (SADHART and ENRICHD) confirm (i) failure of cognitive-behavior therapy to affect survival, (ii) improvement with placebo and usual care, (iii) clinical effect of sertraline, particularly in those with recurrent MDD, (iv) cardiac safety of sertraline. This presentation will highlight the findings of the recently concluded CREATE (Canadian cardiac evaluation of antidepressant and psychotherapy efficacy) study. Results: In a 2-by-2 factorial trial 284 patients with stable CAD were assigned to interpersonal psychotherapy (IPT) or clinical management (CM) and citalopram or placebo for 12 weeks. Citalopram reduced depressive symptoms more than placebo at 6 weeks (p=.01) and at 12 weeks (HAM-D-Hamilton Depression difference 3.3 points, p=.005). Citalopram was efficacious for 43% with recurrent depression compared to those experiencing MDD for the first time. However, there was no additional benefit of adding IPT to CM (HAM-D difference -2.3 points; p=.06), favoring CM over IPT in lowering depressive symptoms. IPT improved depression compared to CM for those subjects with high levels of functional performance. There were 12 cardiovascular and 23 other serious adverse events classified by independent committee and no electrocardiogram effects of the active drug were noted. Conclusion: Citalopram can be considered as a first line treatment of MDD in CAD patients. So far, besides CM, it has not been shown if any form of psychotherapy is indicated for such patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.464 | 0.410 |
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".