Anesthesia and Regional Anesthetic Techniques for Minimally Invasive Direct Coronary Artery Bypass Surgery
Bibliographic record
Abstract
An innovative new approach to coronary revasculariza tion, minimally invasive direct coronary artery bypass is performed via a small anterior minithoracotomy or ministernotomy on a beating heart without the aid of cardiopulmonary bypass. Components of this tech nique, including thoracoscopic video-assisted harvest ing of the internal mammary artery, often with har monic scalpel and potentially even robotic assistance, necessitate prolonged one-lung ventilation. In the ab sence of cardioplegia, myocardial protection during normothermic beating heart surgery poses a challenge. Patient selection is important to avoid intraoperative and postoperative complications. Prolonged single- lung ventilation, incomplete revascularization in hybrid procedures, and limited access for rapid intervention pose challenges with patient management. Conversion to sternotomy may be required in 5% to 7% of patients, and extension of portals over several dermatomal seg ments mandate a versatile analgesic technique. Re gional anesthesia as analgesic adjuvant allows lighter levels of general anesthesia during surgery with mini mal intraoperative hemodynamic changes and a smooth transition to postoperative analgesia. Although a num ber of regional techniques may be used to achieve this goal, thoracic epidural analgesia or continuous percuta neous paravertebral block seem to offer specific advan tages of cardiac sympathectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".