Association Between Severity of Depression and Cardiac Risk Factors Among Women Referred to a Cardiac Rehabilitation and Prevention Clinic
Bibliographic record
Abstract
PURPOSE: Depression comorbid with cardiovascular disease is associated with higher rates of morbidity and mortality, with studies suggesting that this is especially true among women. This study examined depressive symptoms and their relationship to cardiac risk factors among women referred to a women's cardiac rehabilitation and primary prevention program. METHODS: A secondary analysis of data collected between 2004 and 2014 for 1075 women who completed a baseline assessment at the Women's Cardiovascular Health Initiative, a women-only cardiac rehabilitation and prevention program in Toronto, Canada. Descriptive statistics for sociodemographic variables, quality of life (SF-36), and cardiac risk factors were stratified by depression symptom severity using cutoff scores from the Beck Depression Inventory-2nd version (BDI-II) and compared with analysis of variance and χ statistics. Prevalence of antidepressant use among those with moderate to high depressive symptoms was assessed as an indicator of under- or untreated depression. RESULTS: Overall, 38.6% of women scored above the BDI-II cutoff for depression; 23.6% in the moderate or severe range. Socioeconomic status and quality of life decreased with increasing depression severity. Body mass index increased with depressive severity (P < .001), as did the percentage of individuals with below target age predicted fitness (P < .001). Only 39.0% of women in the moderate and severe BDI-II groups were taking antidepressants. CONCLUSION: In this sample, we found a significant prevalence of untreated and undertreated depressive symptoms among women with, or at high risk of developing, cardiovascular disease. Additional strategies are needed to identify these patients early and link them to appropriate treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".