Assessment of perioperative surgical complications in pediatric kidney transplantation: A comparison of pre‐emptive and post‐dialysis recipients
Bibliographic record
Abstract
PURPOSE: To determine whether there is a benefit to pre-emptive kidney transplantation in reducing surgical complications in pediatric population. METHODS: A retrospective review of kidney transplantations in our institution from 2000 to 2015 was performed. Intra- and postoperative complication rates and one-year graft survival were compared in their respective donor type groups (pre-emptive DD vs post-dialysis DD; pre-emptive LD vs post-dialysis LD). RESULTS: A total of 318 patients were identified (pre-emptive DD, n = 21; post-dialysis DD, n = 145; pre-emptive LD, n = 54; post-dialysis LD, n = 98). Between the DD groups, post-dialysis DD group was more likely to be female (P = 0.017). There was no difference in rates of intraoperative complications or graft loss (P = 0.365 and 1.000, respectively). Post-dialysis DD groups were more likely to have postoperative complications (9.5% vs 35.1%, P = 0.023), but no difference in complications with Clavien-Dindo grade 3 or higher was found (P = 0.130). Between the LD groups, post-dialysis LD group was more likely to be females (P = 0.017) and with intrinsic renal (non-urological/structural) ESRD etiology (P = 0.003). There was no difference in rates of intra-and postoperative complications or graft loss (P = 0.353, P = 0.605, and P = 0.616, respectively). CONCLUSIONS: Overall, there are similar perioperative complication rates between pediatric pre-emptive and post-dialysis kidney transplant recipients.
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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 | 0.002 | 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) | 0.001 | 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".