Clinical impact of adjunctive donor microvascular reconstruction on renal transplantation.
Bibliographic record
Abstract
INTRODUCTION: Microvascular reconstruction was incorporated into our donor organ harvesting algorithm for kidneys with anatomic anomalies or injury of the vasculature. The impact of adjunctive microsurgery was appraised in terms of organ availability and graft quality procedures. METHODS: Out of a total of 441 renal transplant procedures performed by one surgeon (JLC) between 1984 and 1997, 104 allografts (83 cadaveric, 21 living related) required ex-vivo microvascular reconstruction. Micro-reconstruction using 2.5-10 X magnification was employed to create a single artery and vein for subsequent in-situ anastomosis. Side-to-side or end-to-side anastomosis was performed, depending on the vascular arrangement. Multiple vessels and those injured during harvesting were reconstructed with a combination of the above techniques. RESULTS: Eleven kidneys had two or more arterial anastomoses; 12 had combination (arterial and venous) anastomoses while 74 required a single micro-reconstruction. In addition, seven kidneys with severely traumatized vessels were salvaged. Average bench surgery times were 30 and 50 minutes for single and multiple reconstructions respectively. Mean warm ischemic time was 29 minutes. Three kidneys were lost due to vascular thrombosis (two venous, one arterial) where in-situ technical difficulties were encountered in all three cases. With mean follow-up of 30 months, 23 kidneys had been lost due to chronic rejection with the remainder functioning. CONCLUSION: Extensive microvascular reconstruction salvaged 30 suboptimal or previously deemed unusable grafts (30/439 = 7%) and facilitated the vascular anastomosis in another 74 cases (17%). The warm ischemic time and the possibility of in-situ technical errors with small-calibre vessels were minimized. This report affirms the contention that microvascular reconstruction should be available as an adjunctive technique for renal transplantation, to maximize the quantity and quality of donor kidneys.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".