Improving Outcomes after Allograft Nephrectomy through Use of Preoperative Angiographic Kidney Embolization
Bibliographic record
Abstract
BACKGROUND: Allograft nephrectomy (AN) has been associated with considerable perioperative morbidity. We aimed to determine if preoperative angiographic kidney embolization (PAKE) to induce graft thrombosis before AN improves outcomes. STUDY DESIGN: We reviewed adult kidney transplant alone patients who underwent AN at a single center from 2002 to 2020 and compared perioperative outcomes for patients with and without PAKE. RESULTS: Eighty patients underwent AN, including 54 (67.5%) with PAKE before AN and 26 (32.5%) with AN alone. PAKE was associated with significantly reduced blood loss (PAKE: mean 266 ± 292 mL vs AN alone: 495 ± 689 mL; p = 0.04) and reduced transfusion requirements (PAKE: mean 0.5 ± 0.8 packed red blood cell units vs AN alone: 1.6 ± 2.6 units; p = 0.004) despite similar preoperative hemoglobin levels. Mean operating time (PAKE: 142 ± 43 minutes vs AN alone: 202 ± 111 minutes; p = 0.001) and length of hospital stay (PAKE: 4.3 ± 2.0 days vs AN alone: 9.3 ± 9.4 days; p = 0.0003) also favored PAKE, as did the surgical complication rate (PAKE: 6/54 [11%] vs AN alone: 9/26 [35%], p = 0.02). Long-term patient survival after AN was comparable in both groups. CONCLUSIONS: PAKE was associated with lower intraoperative blood loss, fewer transfusions, reduced operating time, shorter length of stay, and fewer surgical complications compared with AN alone at our center.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".