Bayesian Inference Supports the Use of Bypass Surgery Over Percutaneous Coronary Intervention To Reduce Mortality in Diabetic Patients with Multivessel Coronary Disease
Bibliographic record
Abstract
Background: Coronary artery bypass graft (CABG) surgery may confer a survival advantage over percutaneous coronary intervention (PCI) in diabetic patients with multivessel coronary artery disease (CAD), but results of individual studies have been mixed. The primary aim of the current study was to compare mortality rates in diabetic patients with multivessel CAD randomized to either or CABG or PCI at 5 years or longest follow-up. Methods: Using a Bayesian approach, we updated a prior probability distribution elicited from 8 clinical trials (N=2024) with the likelihood obtained from the Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease (FREEDOM) (N=1460) to determine whether clinical trial evidence supports the underlying hypothesis that CABG is superior to PCI for diabetics with multivessel CAD. Results: A conjugate normal model comparing mortality rates favored the use of CABG (posterior mean odds ratio [OR] = 0.58, 95% Bayesian credible interval [BCI] = 0.48–0.71). Models weighted by the use of drug-eluting stents also favored the use of CABG over PCI (OR = 0.61, 95% BCI 0.48–0.78), as did models weighted by study age (OR=0.64, 95% BCI 0.52–0.80) or use of arterial conduits (OR=0.64, 95% BCI 0.51–0.81). The results were supported by a Bayesian hierarchical meta-analysis using a non-informative prior distribution (OR=0.55, 95% BCI 0.37–0.76). Conclusions: By integrating evidence from various studies, Bayesian methods directly support the underlying hypothesis that revascularization with CABG improves survival compared with PCI in diabetic patients with multivessel CAD.
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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 | 0.100 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".