Health-Related Quality of Life Impacts upon 5-year Survival after Coronary Artery Bypass Surgery
Bibliographic record
Abstract
Background: Poor preoperative health-related quality of life (HRQoL) has been associated with reduced short-term survival after coronary artery bypass graft surgery (CABG); however, it’s impact on long-term mortality is unknown. This study’s objective was to determine if baseline HRQoL status predicts five-year post-CABG mortality. Methods: This pre-specified, Randomized On/Off Bypass Follow-up Study (ROOBY-FS) sub-analysis compared baseline patient characteristics and HRQoL scores, obtained from the Seattle Angina Questionnaire (SAQ) and Veterans Rand Short Form 36 (VR-36), between five-year post-CABG survivors and non-survivors. Standardized sub-scores were calculated for each questionnaire. Multivariable logistic regression assessed whether HRQoL survey sub-components independently predicted five-year mortality (p < 0.05). Results: Of the 2,203 ROOBY-FS enrollees, 2,104 (95.5%) completed baseline surveys. Significant differences between five-years post-CABG deaths (n = 286) and survivors (n = 1818) included age, history of chronic obstructive pulmonary disease, stroke, peripheral vascular disease, renal dysfunction, diabetes, lower left ventricular ejection fraction, atrial fibrillation, depression, non-white race/ethnicity, lower education status, and off-pump CABG. Adjusting for these factors, baseline VR-36 Physical Component Summary score (PCS) [p = 0.01], VR-36 Mental Component Summary score (MCS) (p < 0.001) and SAQ Physical Limitation score (SAQ-PL) (p = 0.003) were all associated with five-year all-cause mortality. Conclusions: Pre-CABG HRQoL scores may provide clinically relevant prognostic information beyond traditional risk models and prove useful for patient-provider shared decision making and enhancing pre-CABG informed consent.
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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 | 0.003 |
| 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) | 0.002 | 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".