Is the Future of Coronary Arterial Revascularization a Hybrid Approach?
Bibliographic record
Abstract
OBJECTIVE: Hybrid coronary revascularization offers and combines the advantages of both surgical and percutaneous revascularization and eliminates at the same time the disadvantages of both procedures. The objective of this study was to assess graft and stent patency at 6 months, rate of bleeding, intensive care unit and hospital stay, rate of reintervention, and long-term clinical follow-up. METHODS: From March 2004 to November 2015, a total of 203 patients underwent robotic-assisted minimally invasive direct coronary artery bypass graft of the left internal thoracic artery to the left anterior descending artery and PCI of a non-left anterior descending vessel in a single or two stage, at three different centers. Patients underwent 6-month angiographic follow-up. The mean ± SD clinical follow-up was 77.82 ± 41.4 months. RESULTS: Successful hybrid coronary revascularization occurred in 196 of the 203 patients. One hundred forty-six patients underwent simultaneous surgical and percutaneous intervention. Nineteen patients underwent PCI before surgery, and 38 patient underwent PCI after surgery. No in-hospital mortality occurred. The mean ± SD ICU stay was 1 ± 1 days and the mean ± SD hospital stay was 5 ± 2 days. Only 13.3% of the patients required a blood transfusion. Six-month angiographic follow-up has been performed in the 95 patients, and it demonstrated a left internal thoracic artery anastomotic patency of 97.9% and stent patency of 92.6%. A total of 77.8 ± 41.4-month clinical follow-up demonstrated 95.1% survival, 92.6% freedom from angina, and 90.7% freedom from any form of coronary revascularization. CONCLUSIONS: Hybrid coronary revascularization seems to be a promising and safe revascularization strategy. It provides selected patients with an alternative, functionally complete revascularization with minimal surgical trauma and good long-term clinical outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".