Is There a Role for Diagonal Coronary Artery Stenting in Patients Undergoing Robotic Coronary Artery Bypass Graft Surgery?
Bibliographic record
Abstract
BACKGROUND: The efficacy of diagonal coronary artery stenting in patients undergoing robotic left internal thoracic artery-to-left anterior descending (LITA-to-LAD) anastomosis is not well defined. The objective of this study was to assess graft and stent patency in a single-stage hybrid revascularization with LITA-to-LAD anastomosis and PCI to a diagonal coronary artery. METHODS: From 2004 to 2014, a total of 25 patients consented to robotic-assisted LITA harvesting and a small left anterior thoracotomy for off-pump coronary artery bypass anastomosis onto the LAD along with concomitant PCI to the diagonal coronary artery. PCI to the diagonal coronary artery was performed in the same fluoroscopy-equipped hybrid operating room. RESULTS: Patients were on average 66 ± 11 years with 32% female. Pre-operative characteristics of these patients included 8% with a grade 3 or 4 left ventricle, 16% with a recent MI, and 92% with CCS III/IV symptoms. There were no death, one patient required an intra-aortic balloon pump, and one patient required re-operation for bleeding. The average ICU stay was 1.1 ± 0.53 days, and the average hospital stay was 4.6 ± 2.4 days. Fitzgibbon Grade A LITA-to-LAD patency at 6-month follow-up was 100%. As well, at 6-month follow-up the DES to the diagonal coronary artery had a patency rate of 96%. CONCLUSIONS: Single-stage hybrid revascularization strategy for bifurcating lesions of the LAD and diagonal coronary arteries with LITA-to-LAD anastomosis and PCI to a diagonal coronary artery appears to have acceptable clinical results with excellent 6-month angiographic patency results.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".