Off-pump vs. on-pump coronary artery bypass surgery: an updated meta-analysis and meta-regression of randomized trials
Bibliographic record
Abstract
AIMS: The benefits of off-pump coronary artery bypass (OPCAB) continue to be debated, in part due to the fact that pooled effects fail to consider differences in trial and patient characteristics. We sought to analyse the contemporary evidence for OPCAB vs. conventional coronary artery bypass (CCAB), incorporating recent larger trials, and adjusting for differences in trials using a technique known as meta-regression. METHODS AND RESULTS: We systematically reviewed MEDLINE, EMBASE, and the Cochrane database for published and unpublished randomized trials of OPCAB vs. CCAB in which 30-day or in-hospital clinical outcomes were reported. The outcomes of interest were: all-cause mortality, stroke, and myocardial infarction. In addition to measuring the pooled treatment effects using a random effects meta-analysis model, we measured the effect of selected trial-level factors on the effects observed using the meta-regression technique. Fifty-nine trials were included, encompassing 8961 patients with a mean age of 63.4 and 16% females. There was a significant 30% reduction in the occurrence of post-operative stroke with OPCAB [risk ratio (RR) 0.70, 95% CI: 0.49-0.99]. There was no significant difference in mortality (RR: 0.90, 95% CI: 0.63-1.30) or myocardial infarction (pooled RR: 0.89, 95% CI: 0.69-1.13). In the meta-regression analysis, the effect of OPCAB on all of the clinical outcomes was similar regardless of mean age, proportion of females in the trial, number of grafts per patient, and trial publication date. CONCLUSION: Our meta-analysis incorporating recent trials suggests that there appears to be a beneficial effect of OPCAB on stroke. Moreover, our meta-regression does not support the hypothesis that differences in study populations are responsible for the observed outcomes, although pooled individual patient-data would be better suited to confirm these findings.
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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 | 0.039 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.070 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".