Patients With Depressive Symptoms Have Lower Health Status Benefits After Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: Depression is an established independent prognostic factor for mortality, readmission, and cardiac events after CABG surgery. However, limited data exist on whether depression influences functional outcomes after CABG. METHODS AND RESULTS: We followed 963 patients who underwent first CABG between February 1999 and February 2001. At baseline and at 6 months after CABG, we interviewed patients to assess depressive symptoms using the Geriatric Depression Scale (GDS) and physical function using the Short Form-36 Physical Component Scale (PCS). The patient's physical function was considered improved if the PCS score increased > or =5 points at 6 months. Patients with high GDS scores were younger, were more often female, and had worse physical function and higher comorbidity than patients with low GDS scores. Rates of improvement in physical function were 60.1% for a GDS score <5 (below 75th percentile), 49.8% for a GDS score between 5 and 9 (75th to 90th percentile), and 39.7% for a GDS score > or =10 (> or =90th percentile; P=0.002 for the trend). Depressive symptoms remained a significant independent predictor of lack of functional improvement after adjustment for severity of coronary artery disease, angina class, baseline PCS score, and medical history. A GDS score > or =10 was a stronger inverse risk factor for functional improvement after CABG than such traditional measures of disease severity as previous myocardial infarction, heart failure on admission, history of diabetes, and left ventricular ejection fraction. CONCLUSIONS: Higher levels of depressive symptoms at the time of CABG are a strong risk factor for lack of functional benefits 6 months after CABG.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".