Predictors and Outcomes of Sternotomy Conversion and Cardiopulmonary Bypass Assistance in Minimally Invasive Coronary Artery Bypass Grafting
Bibliographic record
Abstract
OBJECTIVE: This work's objective was to identify the determinants of conversion of minimally invasive coronary artery bypass grafting to sternotomy, with and without cardiopulmonary bypass assistance, and to compare clinical outcomes in patients who needed conversion. METHODS: This is a prospectively collected data on patients who underwent minimally invasive coronary bypass done by a single surgeon from February 2005 to September 2014. Statistical analyses were expressed as mean values ± standard deviation or proportions. RESULTS: The total number of patients was 266, with an average age of 62 years. The median number of grafted territories was 2, higher in those with pump assistance (median, 3 grafts; P ≤ 0.01). Predictors for use of cardiopulmonary bypass included diabetes, 3-vessel disease, left circumflex involvement, and small target vessels (P < 0.05). The rate for sternotomy conversion was 3.8%. Risk factors for conversion to sternotomy included smoking, preoperative bradycardia (<50 beats per minute), low intraoperative ejection fraction, inability to tolerate one-lung ventilation, inadequate surgical exposure, and hemodynamic instability. Postoperative complications included superficial thoracotomy infection (3%), sternotomy infection (10%), new atrial fibrillation (3%), and need for blood transfusion (14%). Twelve patients (5%) developed left-sided pleural effusion that required drainage. There were no perioperative deaths, major adverse cardiac event, or stroke. CONCLUSIONS: Minimally invasive coronary bypass grafting with conversion to sternotomy and use of cardiopulmonary bypass is safe. Conversions may be alleviated by an effort to optimize modifiable risk factors and the adequacy of surgical exposure. These data may help develop objective selection criteria to identify patients who are excellent candidates for the procedure.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".