Do Differences in Repeat Revascularization Explain the Antianginal Benefits of Bypass Surgery Versus Percutaneous Coronary Intervention?
Bibliographic record
Abstract
BACKGROUND: Patients with multivessel coronary disease treated with coronary artery bypass graft (CABG) have less angina than those treated with percutaneous coronary intervention (PCI); however, there is uncertainty as to the mechanism of greater angina relief with CABG and whether more frequent repeat revascularization in patients treated with PCI could account for this treatment difference. METHODS AND RESULTS: In the Synergy between percutaneous coronary intervention (PCI) with TAXUS and Cardiac Surgery trial, 1800 patients with 3-vessel or left main coronary artery disease were randomized to CABG or PCI with paclitaxel-eluting stents. Health status was assessed at baseline, 1, 6, and 12 months, using the Seattle Angina Questionnaire and the Medical Outcomes Study Short Form General Health Survey, and the association between repeat revascularization and health status during follow-up was assessed using longitudinal models. In adjusted analyses, patients who underwent repeat revascularization had worse angina frequency scores than patients who did not in both treatment groups, with differences of 8.5 points at 6 months and 3.1 points at 12 months in patients treated with PCI and 19.8 points at 6 months and 11.2 points at 12 months in patients with patients treated with CABG. Among patients who did not require repeat revascularization, the adjusted effect of CABG versus PCI on 12-month angina frequency scores was nearly identical to the overall benefit in the intention-to-treat analysis. CONCLUSIONS: Among patients with multivessel coronary artery disease treated with PCI or CABG, the occurrence of repeat revascularization during follow-up did not fully explain the antianginal benefit of CABG in the overall population. The differential association between repeat revascularization and anginal status, according to the type of initial revascularization procedure, suggests that this end point should play a limited role in any direct comparison of the 2 treatment strategies.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".