Vascular inflow after renal transplantation: Does the arteriotomy technique impact early allograft perfusion and function?
Bibliographic record
Abstract
BACKGROUND: There are two main techniques for arterial reconstruction in RT: TA using a stab longitudinal incision which creates an elliptical opening and AP which fashions a circular defect. We hypothesized that AP creates a natural anastomosis lumen, similar to the donor renal artery, which optimizes RT perfusion. METHODS: A retrospective review of a single-institution database was performed between 2000 and 2018. Twenty patients who underwent AP arteriotomy were compared to 40 TA-matched controls. Data were collected on creatinine (preoperative, nadir, and time to nadir), and DUS RI and PSV at 1 week, 3 months, and 6-12 months post-RT. RESULTS: ttNC was shorter in the AP group (5 ± 4 vs 12 ± 13 days; P = .03). PSV at 1 week was lower in the AP group (186 ± 65 cm/s vs 232 ± 89 cm/s; P = .04). There was no difference in nadir creatinine value (P = .26), preoperative creatinine (P = .66), and initial postoperative creatinine (P = .80). RI at week 1 were not different between groups (P = .37). Follow-up DUS showed the difference in PSV between groups became non-significant (1 month P = .50 and 6-12 months P = .53). CONCLUSIONS: AP arteriotomy in RT improves early perfusion and function parameters (ttNC and initial PSV) as compared to TA. AP arteriotomy optimizes early allograft reperfusion, which may have important long-term implications and deserves further evaluation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".