Reflections on Depression as a Cardiac Risk Factor
Bibliographic record
Abstract
OBJECTIVE: Major North American cardiology organizations do not currently list depression among the officially recognized cardiac risk factors, yet many behavioral medicine specialists believe depression to be an important risk. We wondered what was missing from the available data. METHODS: The Medline, Current Contents, and PsychInfo databases were used to perform a systematic review of the literature linking depression and depressive symptoms with cardiac disease outcomes. Because of previous reviews, we paid particular attention to publications from 2001 to 2003. RESULTS: We identified 21 etiologic and 43 prognostic publications that had prospective designs, used recognized measures of depression, and included objective outcome measures. We also identified 79 review articles. In addition to issues of sample size, sample characteristics, and timing of measures, we noted heterogeneity in the definitions of depression, frequent repeat publications from the same data sets, heterogeneity of outcome measures, a variety of approaches for covariate selection, and a preponderance of review articles, all factors that cannot help to convince skeptics. CONCLUSIONS: Despite these issues, the bulk of the data from prospective studies with recognized indices of depression and objective outcome measures is supportive of depression as a cardiac risk factor.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| 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".