Abstract 12028: Long-Term Repeat Intervention Incidence in Octogenarians After Robotic-Assisted Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Introduction: We aim to analyze long-term repeat intervention incidence in octogenarians undergoing robotic-assisted coronary artery bypass grafting (CABG). Hypothesis: We hypothesize that robotic-assisted CABG has a low incidence of repeat intervention compared to other clinical trials. Methods: All consecutive octogenarians (500 patients) undergoing robotic-assisted left internal thoracic artery (LITA) harvesting and CABG through a 4-cm left minithoracotomy with LITA to left anterior descending (LAD) artery anastomosis between 2005-2021 in a single center were included. Hybrid coronary revascularization (HCR) consists in robotic-assisted CABG followed by percutaneous coronary intervention (PCI) with DES in a non-LAD target within 7-days from the operation. Results: Preoperatively, mean age was 83 (±2.9) year-olds, and mean STS-PROM risk score was 3.6% (2.1- 6.6). Intraoperatively, 478 (95.6%) patients received 1 LITA, 14 (12.8%) received bilateral ITA, and 8 (1.6%) a saphenous venous graft. A total of 303 (60.6%) patients received multiple arterial revascularizations while 140 (28%) received HCR. A total of 376 (75.2%) patients were extubated in the operating room. Postoperatively, 2 (0.4%) patients experienced non-fatal stroke, 6 (1.2%) had reoperation for bleeding, and 12 (2.4%) had renal failure. There was no 30-day mortality while 1 (0.2%) patient had PCI with DES on LITA-LAD anastomosis due to graft failure. Mean-follow-up was 2.9-years while incidence of all-cause death was 103 (20.6%) patients, MACCE 136 (27.2%), stroke 8 (1.6%), myocardial infarction 16 (3.2%), and repeat intervention with stents 39 (7.8%) were low. There was no repeat surgical intervention while PCI with drug-eluting stents for new coronary disease was 15/39 (38.4%) patients. Conclusions: Robotic-assisted CABG in octogenarians evidenced a 7.8% incidence of repeat intervention with stents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".