Depression and cardiac risk: present status and future directions
Bibliographic record
Abstract
Since the mid-1980s, an impressive body of epidemiological research has examined links between depression and coronary heart disease (CHD). Depression is more common in patients with CHD than in those without heart disease, with ≥20% of hospitalised patients after a myocardial infarction (MI) meeting modified psychiatric criteria for major depressive disorder (MDD).1 While available data suggest that depression rates are lower in patients with stable CHD than in hospitalised patients, depression is still more common than in the general community. Depression is associated with increased chances of developing CHD in apparently healthy subjects. In patients with CHD depression predicts cardiac admissions and death, increased healthcare costs and utilisation of services.2 3 There is evidence of an increased cardiac risk associated with measures of depression symptoms as well as with diagnosed MDD, and of a dose–response relationship between depression severity and prognosis in patients with CHD. Many plausible biological explanations have been suggested. The quantity and strength of the epidemiological data is comparable to that leading to the general acceptance of several other cardiac risk factors. Why, then, is depression not considered a major risk factor? Should it be? Updating our previous systematic reviews3 4 to include publications through 15 September 2009, we found reports based on prospective studies, using established measures of depression published from at least 28 distinct cohorts each made up of more than 500 people who were apparently CHD-free at baseline, and at least 59 separate studies that examined depression as a predictor of risk for poor prognosis in samples of at least 100 patients with known CHD. The great majority of the 130 articles based on these studies document statistically significant relationships between depression and cardiac outcomes, and most, but not all, demonstrate that these relationships remain significant after statistical adjustment for a …
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".