Reflections on Depression as a Cardiac Risk Factor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it