Depression and the Risk of Myocardial Infarction and Coronary Death
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
Findings regarding the association between depression and risk of coronary heart disease are inconsistent. We aimed to assess the association between depression and risk of myocardial infarction (MI) and coronary death through a meta-analysis.We performed an electronic literature search of MEDLINE, EMBASE, PsycINFO, ISI Web of Science, and Scopus databases through August 1, 2015, and manual search of the references of the eligible papers and related review articles. Two investigators independently conducted study selection and data abstraction. Disagreement was resolved by consensus. Confounder-adjusted hazard ratios (HRs) were pooled using a random-effects model. Heterogeneity was evaluated using the Cochran Q statistic and Higgins index. Publication bias was assessed by funnel plot and Egger test. Study quality was appraised with the Newcastle-Ottawa Scale.Among 19 eligible cohort studies including 323,709 participants, 8447 cases of MI and coronary death were reported during follow-up ranging from 4 to 37 years. The pooled adjusted HRs for patients with depression (vs those without) were 1.22 (95% CI, 1.13-1.32) for combined MI and coronary death, 1.31 (95% CI, 1.09-1.57) for MI alone (9 studies), and 1.36 (95% CI, 1.14-1.63) for coronary death alone (8 studies). The increased risk of MI and coronary death associated with depression was consistent using modified inclusion criteria, across most subgroups, and after adjusting for possible publication bias.Depression is associated with a significantly increased risk of MI and coronary death. Effective prevention and treatment of depression may decrease such risk.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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