Can Minimally Invasive Coronary Artery Bypass Grafting be Initiated and Practiced Safely?
Bibliographic record
Abstract
OBJECTIVE: We examined the effects of learning curve on clinical outcomes and operative time in minimally invasive coronary artery bypass grafting (MICS CABG). METHODS: We studied 210 consecutive MICS CABG cases performed by the same surgeon, composed of 3 cardiopulmonary bypass (CPB)-assisted single-vessel small thoracotomy (SVST), 87 off-pump SVST, 51 CPB-assisted multivessel small thoracotomy (MVST), and 69 off-pump MVST. For each MICS CABG technique, the frequency of early clinical events (mortality, reopening, stroke, myocardial infarction, and revascularization) was compared between the first 25 cases and the remainder. Logarithmic curve regression analysis and a cumulative summation technique were performed to assess the correlation between operative time and the performed number of each technique. RESULTS: There was no mortality, and there were 10 conversions to standard sternotomy, all of which were intended as off-pump MVST (P < 0.001, vs other procedures). Experience was otherwise not associated with perioperative outcome. However, experience numbers correlated with operative time in off-pump SVST and off-pump MVST (122 ± 30 minutes, R = 0.18, P < 0.001, and 241 ± 80 minutes, R = 0.38, P < 0.001, respectively) but not in CPB-assisted MVST (258 ± 44 minutes, R = 0.004, P = 0.7). No complications occurred as a result of CPB assistance. CONCLUSIONS: Minimally invasive coronary artery bypass grafting can be safely initiated, with a very low perioperative risk. Pump assistance is a good strategy to alleviate some of the learning curve and avoid conversions to sternotomy when initiating a multivessel MICS CABG program.
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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 | 0.019 |
| 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 | 0.001 | 0.001 |
| 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".