Quality of Life Following Cardiac Surgery: Impact of the Severity and Course of Depressive Symptoms
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to examine the impact of the severity and course of depressive symptoms on change in quality of life (QOL) 6 months after cardiac surgery. METHODS: Ninety patients were interviewed before heart surgery and 2 and 6 months after surgery. Depressive symptoms were assessed using the Beck Depression Inventory, and QOL was assessed using physical and psychosocial functioning indices derived from the Medical Outcomes Study instrument. Multiple regression examined the effects of the severity and course of depressive symptoms on QOL adjusting for demographic and biomedical predictors. RESULTS: Higher levels of presurgical depressive symptoms predicted poorer physical functioning after cardiac surgery. A similar effect on psychosocial functioning fell short of significance. An increase in depressive symptoms 2 months after surgery was significantly predictive of poorer physical and psychosocial functioning at 6 months. The effect of increased depressive symptoms on psychosocial functioning was significantly stronger in patients with high presurgical Beck Depression Inventory scores. CONCLUSIONS: Both preoperative depressive symptoms and postoperative increases in depressive symptoms seem associated with poorer QOL 6 months after cardiac surgery. Further examination of these associations and the mechanisms they reflect may provide a basis for guiding treatment decisions before and after coronary artery bypass graft surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".