Relationship between cardiopulmonary fitness and depressive symptoms in cardiac rehabilitation patients with coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: To identify independent predictors of depressive symptoms in a cohort of patients with coronary artery disease entering cardiac rehabilitation. DESIGN: Cross-sectional cohort study. PATIENTS AND METHODS: Consecutive patients entering a cardiac rehabilitation and secondary prevention programme underwent screening for depressive symptoms using the Center for Epidemiological Studies Depression (CES-D) scale and cardiopulmonary fitness testing to quantify peak oxygen consumption. RESULTS: Of the 366 patients with coronary artery disease, 22.3% reported at least mild (CES-D > or = 16) and 10.4% reported significant (CES-D > or = 23) depressive symptoms. Antidepressant medications were being used by 6.3% of patients. Sociodemographic, cardiopulmonary and cardiac characteristics, and medical co-morbidities previously associated with depression accounted for 14.7% of the variance in a multiple linear regression model (F = 8.713, p < 0.001) predicting CES-D scores. Significant independent predictors of CES-D scores were lower peak oxygen consumption, younger age, female sex, lower maximum diastolic blood pressure, angina pectoris and antidepressant use. CONCLUSION: Reduced physical fitness, younger age, female sex and ischaemic symptoms of coronary artery disease predict higher depressive symptoms in patients entering cardiac rehabilitation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".