Beating heart multi-vessel minimally invasive direct coronary artery bypass grafting: techniques and pitfalls
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) has been changing since its initial reports. There has been a shift from standard CABG, which uses sternotomy and a single left internal mammary to the left anterior descending artery (LAD) grafting, plus vein grafts to other targets, and performed on bypass with aortic cross-clamping. We now have CABG using multiarterial grafts, avoiding manipulation of the aorta, and through a minimally invasive approach. Beating heart Multi-vessel Minimally Invasive Coronary Artery Bypass (Multi-vessel MICS CABG) has emerged as an attractive alternative in coronary revascularization. The minimally invasive approach mitigates some of the risks and long recovery associated with the more invasive standard full sternotomy approach. It decreases the rates of wound infection, transfusion, post-operative pain, time of recovery and sternal dehiscence, while maintaining the same outcomes as the standard approach with full sternotomy. There are multiple centers around the world that have reported safe and good outcomes with Multi-vessel MICS CABG, including the initial report from our center joint with Staten Island, NY. The aim of this paper is to describe the technique and pitfalls of Multi-vessel MICS CABG as used at our center and go over patient selection and outcomes. A video and several pictures are shown to facilitate the understanding and learning of this technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".