A comparison of cryopreserved and autologous vein grafts in aortic reconstruction
Bibliographic record
Abstract
Our objective was to determine operation duration, length of stay, and outcome differences between patients requiring aortic reconstruction with autologous vein grafts and cryopreserved vein grafts. A retrospective review of patients who underwent aortic reconstruction using autologous vein grafts and cryopreserved vein grafts between January 1, 2012, and February 1, 2024, was completed. Patient demographics and comorbidities, presenting symptoms, and postoperative outcomes were reviewed. Operative variables and patient outcomes were compared using the Student t test and odds ratios. Outcome variables were further stratified by secondary injury requiring additional repair in addition to reconstruction, such as aortoenteric fistulas or damage to surrounding structures, primary vs secondary infection, type of surgical repair, and number of veins required for reconstruction. A total of 33 patients were included in the study, with 17 patients receiving cryopreserved grafts and 16 autologous grafts. Of these, 5 patients had primary infections, and 26 had secondary infections. There were no statistically significant differences in demographics or comorbidities between the groups. In patients requiring reconstruction with two veins, cryopreserved vein use was associated with decreased operative times, irrespective of concurrent injury (271 ± 22 minutes vs 359 ± 26 minutes; P < .05). In uncomplicated repairs without secondary injury, cryopreserved vein reconstructions also had significantly shorter operative times (261 ± 21 minutes vs 324 ± 20 minutes; P < .05). Cryopreserved vein use did not impact mortality or postoperative complication rates significantly up to the 2-year follow-up. Using cryopreserved veins decreased operative time for patients without concomitant injury and in cases requiring two harvested autologous veins. Postoperative outcomes were similar between the groups.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".