Reason and Timing for Conversion to Sternotomy in Robotic-Assisted Coronary Artery Bypass Grafting and Patient Outcomes
Bibliographic record
Abstract
OBJECTIVE: Conversion to sternotomy is a primary bailout method for robotically assisted coronary artery bypass grafting procedures. The aims of this study were to identify the primary reasons for conversion from robotically assisted coronary artery bypass grafting to sternotomy and to evaluate the in-hospital outcomes in such patients. METHODS: Prospectively collected data from February 2004 to April 2017 were reviewed for 72 patients (56 men; mean age = 63.8 years) who required conversion to sternotomy during a robotically assisted coronary artery bypass grafting procedure with planned endoscopic left internal thoracic artery harvest and anastomosis to the left anterior descending on the beating heart. RESULTS: The overall rate of conversion was 12.4% (72/581). Conversions occurred either during attempted endoscopic left internal thoracic artery harvest (31.9%), during endoscopic left anterior descending isolation (40.3%), during manual isolation and anastomosis of the left anterior descending (19.4%), or after anastomosis due to unsatisfactory flow (8.3%). Overall, the most common reason for conversion was an intramyocardial left anterior descending (43.1%). The median stay in the intensive care unit was 1 day (range = 0-20) and the median hospital length of stay was 5 days (range = 3-43). In-hospital complications included new atrial fibrillation (16.7%), need for blood transfusion (20.8%), mediastinitis (4.2%), postoperative myocardial infarction (2.8%), exploration for bleeding (2.8%), and 1 in-hospital death. CONCLUSIONS: The reasons for conversion were primarily related to anatomical factors that created difficulties for endoscopic left internal thoracic artery harvesting and left anterior descending identification. Patients who required conversion to sternotomy from robotically assisted coronary artery bypass grafting demonstrated acceptable outcomes and low complication rates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".