Effect of coronary artery bypass grafting on quality of life: a meta-analysis of randomized trials
Bibliographic record
Abstract
AIMS: We conducted a systematic review and meta-analysis to evaluate temporal trends in quality of life (QoL) after coronary artery bypass grafting (CABG) surgery in randomized clinical trials, and a quantitative comparison from before surgery to up to 5 years after surgery. METHODS AND RESULTS: We searched MEDLINE, CINAHL, EMBASE, Cochrane Library, and PsycINFO from 2010 to 2020 to identify studies that included the measurement of QoL in patients undergoing CABG. The primary outcome was the Seattle Angina Questionnaire (SAQ), and secondary outcomes were the 36-item Short Form Health Survey (SF-36) and EuroQol Questionnaire (EQ-5D). We pooled the means and the weighted mean differences over the follow-up period. In the meta-analysis, 2586 studies were screened and 18 full-text studies were included. There was a significant trend towards higher QoL scores from before surgery to 1 year post-operatively for the SAQ angina frequency (AF), SAQ QoL, SF-36 physical component (PC), and EQ-5D, whereas the SF-36 mental component (MC) did not improve significantly. The weighted mean differences from before surgery to 1 year after was 24 [95% confidence interval (CI): 21.6-26.4] for the SAQ AF, 31 (95% CI: 27.5-34.6) for the SAQ QoL, 9.8 (95% CI: 7.1-12.8) for the SF-36 PC, 7.1 (95% CI: 4.2-10.0) for the SF-36 MC, and 0.1 (95% CI: 0.06-0.14) for the EQ-5D. There was no evidence of publication bias or small-study effect. CONCLUSION: CABG had both short- and long-term improvements in disease-specific QoL and generic QoL, with the largest improvement in angina frequency.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.060 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".