Endarterectomy may be an effective additional treatment for three diffuse coronary artery disease complicated with diabetes
Bibliographic record
Abstract
Objective In order to evaluate the clinical efficacy of coronary endarterectomy (CE) and coronary artery bypass grafting (CABG) in patients with diabetes complicated with three diffuse coronary artery stenosis. Methods A retrospective analysis was conducted on 460 patients with diabetes mellitus and diffuse three-vessel coronary artery disease who underwent CABG in our department from September 2015 to December 2021. The patients were divided into two groups according to whether they underwent CE: the simple CABG group (group A, n = 254) and the CABG combined CE group (group B, n = 206). The perioperative outcomes, recurrent angina pectoris during 1-year follow-up, and the patency rate of the grafted vessel in coronary CT angiography were compared between the two groups. Results There was no significant difference in the 30 days mortality rate between the two groups (2.3% vs 2.4%, p < 0.05). Group A had a shorter operation time [(3.55 ± 0.59) h versus (4.35 ± 0.65) h], less bypass grafts [(2.72 ± 0.83) versus (3.65 ± 0.72) vessels/case], a lower incidence of perioperative myocardial infarction (7.1% vs 12.6%), and a lower number of patent graft vessels at 1-year follow-up [(2.15 ± 0.42) versus (2.88 ± 0.68) vessels/case] compared with group B (all p < 0.05). Group A had a higher incidence of recurrent angina during follow-up (14.49% vs 6.47%) ( p < 0.05). Although there was no significant difference in the incidence of MACCE events between the two groups, the probability of revascularization was higher in group A. Conclusion Compared with single CABG, combined CE in patients with diabetes mellitus and diffuse three-vessel coronary artery disease can achieve more complete revascularization, reduce the recurrence of angina pectoris and the needing of postoperative revascularization, but the incidence of perioperative myocardial infarction is higher.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".