Cardiovascular Health and Depression
Bibliographic record
Abstract
Research has shown that depression increases the likelihood that otherwise healthy people will develop ischemic heart disease (IHD) and worsens the prognosis of patients who already have IHD. Moreover, concerns about safety (e.g., cardiac side effects, drug-drug interactions) have caused physicians to be hesitant about using antidepressant agents in patients with IHD. This article is based on a recent roundtable of experts who met to discuss risk, diagnosis, and treatment options for depression in patients with IHD. This article reviews clinical and epidemiological studies that have described a link between depression and the subsequent development of IHD and have examined the role of depression as a predictor of cardiac events in patients with existing IHD. The article addresses the issue of whether depression can be safely and efficaciously treated both in patients with stable IHD and in those with acute coronary syndromes. The authors discuss safety issues related to the potential for interactions between antidepressants and cardiovascular medications, the use of nonpharmacologic treatment options such as psychosocial interventions, and the effect of antidepressant therapy on quality of life in patients with IHD. The article concludes with practical clinical guidance concerning the management of depression in patients who have recently experienced myocardial infarction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".