1424 Identifying Modifiable Risk Factors that Contribute to Reported Depressive Symptomatology at 1 Year Following Acute Myocardial Infarction
Bibliographic record
Abstract
Purpose: Studies have identified the prevalence of major depression in patients with cardiovascular disease between 16 and 23%, while 65% of patients manifest symptoms of major or minor depression following an acute myocardial infarction (AMI). Previous work has demonstrated that controlling for 1-year follow-up Beck depression scores substantially attenuated the differences noted in the health related quality of life outcomes. Although studies have identified an association between depressive symptomatology at entry and poorer outcomes, the relationship between CAD, clinical, socio-demographic at baseline, and depressive symptoms at 1 year following an AMI remains unclear. The objective of this study was to examine the relationship of clinical and socio-demographic variables as they relate to reported depressive symptomatology at 1 year. Methods: Patients with documented AMI, admitted to 5 tertiary care and 5 community hospitals in Quebec, Canada were recruited within 2–3 days of admission. Trained nurses collected demographic and clinical information from medical records and patients completed a mailed questionnaire that included the Beck Depression Inventory at baseline and the 1-year anniversary of their admission for AMI. Information on vital status for patients lost to follow-up was obtained from a central death registry.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".