Comparing the use of carotid-subclavian bypass and subclavian-carotid transposition for zone 2 aortic repair
Bibliographic record
Abstract
Objective Left subclavian artery revascularization is indicated in most cases of zone 2 aortic repair. This is commonly accomplished via left carotid to subclavian bypass (SB) or transposition. Proponents cite benefits of each procedure; however, long-term comparative studies are lacking. Our objective is to compare perioperative and long-term outcomes for patients undergoing carotid SB vs transposition in zone 2 aortic repair. Methods All patients from a single tertiary care center undergoing carotid subclavian bypass or transposition over a 17-year period were identified. Patients were assigned to a carotid SB or subclavian to carotid transposition (SCT) cohort. Retrospective analysis of perioperative and long-term results was completed. Two univariate logistic regression models were fitted to assess mortality and hoarseness and to quantify differences between the two cohorts. Results A total of 121 patients underwent subclavian artery revascularization during the study period. Sixty-two underwent SCT (51.2%), and fifty-nine (48.8%) underwent carotid SB. There was no significant difference in perioperative mortality, stroke, or paraplegia between cohorts. Postoperative hoarseness was significantly higher in the SCT cohort (37.1% vs 13.6%; P = .003), but there were no significant differences at the 6-month mark (9.7% vs 3.4%; P = .27). Long-term results revealed no significant differences with regards to thrombosis of revascularization or need for reintervention. Logistic regression demonstrated that patients who underwent SCT had nearly four times higher odds of perioperative hoarseness compared with those who underwent carotid SB (odds ratio, 3.76; 95% confidence interval, 1.52-9.30). Conclusions Perioperative outcomes are similar between carotid SB and SCT, with a higher rate of hoarseness in the transposition patients. Long-term results show that most cases of hoarseness resolve, and there was no significance difference between groups at long-term follow-up.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".