Risk Factors for Coronary Events After Robotic Hybrid Off-Pump Coronary Revascularization
Bibliographic record
Abstract
OBJECTIVES: The impact of long-term complications after robotic hybrid coronary revascularization (HCR), including persistent angina, repeat revascularization, and myocardial infarction (MI), remains limited. This study aims to determine the risk factors for coronary events after robotic HCR and their time-varying effects on outcomes. METHODS: We identified all consecutive patients who underwent robotic HCR at our institution. Baseline characteristics were explored as possible risk factors for angina, MI, and repeat revascularization with stents at any time during the follow-up. RESULTS: A total of 875 patients (mean age 71.1 ± 11.1 years) were included. After a median follow-up of 3.32 years (IQR 1.18-6.34 years), angina occurred in 134 patients (15.3%), repeat revascularization with stents in 139 patients (15.8%), and MI in 36 patients (4.1%). The hazard rates for all outcomes increased with follow-up time, with a notable early rise around two years of follow-up for angina and, to a lesser extent, repeat revascularization. The risk factors were the lack of radial artery graft use, black race, diabetes, obesity, chronic obstructive pulmonary disease, low ejection fraction <50%, severe left main coronary artery stenosis (>50%), and more than three-vessel disease. CONCLUSIONS: Optimization of modifiable periprocedural risk factors may positively impact long-term prognosis in patients undergoing robotic HCR.
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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".