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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".