Review of minimally invasive coronary artery bypass grafting
Bibliographic record
Abstract
OBJECTIVES: Minimally invasive coronary artery bypass grafting (CABG), defined broadly as surgical revascularization via any sternotomy-sparing approach. Here, we provide an overview of minimally invasive CABG targeted to cardiologists, cardiac surgeons and other clinicians involved in the care of patients with coronary artery disease (CAD). METHODS: A narrative review of the literature on minimally invasive CABG was performed. RESULTS: Minimally invasive CABG was first described over 20 years ago, yet uptake has been slow and available data are limited. The most common iteration of minimally invasive CABG is a single-vessel CABG (left internal mammary artery to the left anterior descending artery) performed without the cardiopulmonary bypass machine via mini-thoracotomy. However, in patients with multivessel CAD, other options include minimally invasive multivessel CABG and hybrid revascularization (minimally invasive CABG with percutaneous coronary intervention). Patient selection and preoperative planning are paramount. Observational studies and small randomized controlled trials demonstrate that minimally invasive CABG is associated with reduced rates of blood transfusion, surgical site infections, lengths of intensive care unit and hospital stays, and time to return to full activity with promising late outcomes. Finally, we describe future areas for growth, including ongoing clinical trials, gaps in evidence and pragmatic considerations for surgeons interested in starting a minimally invasive CABG programme. CONCLUSIONS: Minimally invasive CABG can expand the armamentarium of revascularization techniques available for the ageing and increasingly complex population of patients with CAD.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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 | 0.001 |
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".