Minimally invasive coronary artery bypass grafting
Bibliographic record
Abstract
PURPOSE OF REVIEW: Minimally invasive coronary artery bypass grafting (MICS CABG) consists of single-vessel or multivessel revascularization via a small left thoracotomy, and has been proposed as an alternative to a standard sternotomy approach. The purpose of this article is to examine the current status of MICS CABG and discuss its future directions. RECENT FINDINGS: Experience in the first 450 cases was reported in 2009, and established the efficacy and safety of a small thoracotomy approach for multivessel and single-vessel revascularization. In addition to earlier recovery and rehabilitation, MICS CABG is associated with fewer transfusions and fewer wound infections than off-pump CABG. Recently, the MICS CABG Patency Study showed excellent graft patency in patients assessed by 64-slice computed tomography angiography 6 months after operation. We also showed that the use of cardiopulmonary bypass assistance may help alleviate some of the learning curve inherent in this operation. SUMMARY: MICS CABG has developed into a reproducible, high-quality, complete surgical revascularization alternative to conventional CABG. Preservation of sternal integrity allows patients to recover earlier, require fewer transfusions, and experience fewer infections. Further research on expanding the applicability of MICS CABG and enhancing its advantages over conventional CABG is warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".