Minimally Invasive Multivessel Coronary Surgery and Hybrid Coronary Revascularization: Can We Routinely Achieve Less Invasive Coronary Surgery?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) is the gold standard in managing severe coronary artery disease. However, it is associated with prolonged recovery and potential complications, in part due to the invasiveness of the procedure. Less invasive CABG techniques attempt to improve the quality and quantity of life in the same way as surgical revascularization but with fewer complications. Minimally invasive coronary surgery (MICS) through a small thoracotomy allows for complete revascularization with good results in graft patency. Perioperative mortality is low, and there is decreased need for blood transfusion, lower surgical site infection rates, and an earlier return to full physical function. Hybrid coronary revascularization (HCR) attempts to combine the advantages of coronary artery bypass grafting with those of percutaneous coronary intervention. Several studies have shown that HCR provides better short-term outcomes with regard to decreased ventilation and ICU time, reduced need for blood transfusion, and shortened hospital stay. However, the rates for major adverse cardiovascular events and mortality are comparable to conventional CABG, except for patients with a high SYNTAX score who displayed increased mortality rates. There is also strong evidence of a higher need for repeat revascularization with HCR compared to CABG. Overall, MICS and HCR appear to be viable alternatives to conventional CABG, offering a less invasive approach to coronary revascularization, which may be especially beneficial to high-risk patients. This article discusses approaches that deliver the advantages of minimally invasive surgical revascularization that can be adapted by surgeons with minimal investment with regards to training and infrastructure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it