A comparison of post‐transplant renal function in pre‐emptive and post‐dialysis pediatric kidney transplant recipients
Bibliographic record
Abstract
PURPOSE: Little is known regarding post-transplant renal function following pediatric pre-emptive KT. Therefore, this study aims to determine whether there is a difference in 1 year post-transplant renal function outcomes between pre-emptive and post-dialysis KT in pediatric transplant recipients. METHODS: ), acute rejection episodes within 1 year, and hospitalization within 1 year were compared to between groups in their respective donor types (pre-DD vs post-DD; pre-LD vs post-LD). RESULTS: The 324 patients were identified (21 pre-DD, 151 post-DD, 54 pre-LD, and 98 post-LD). Post-DD group had more females (P = 0.018) and post-operative complications (P = 0.023), although there was no difference in complications requiring intervention (P = 0.129). Post-LD patients were more likely to be females (P = 0.017) and those with intrinsic renal (non-urological/structural) ESRD etiology (P = 0.003). The 1-year eGFR was similar between pre-DD and post-DD groups (70.3 [IQR 53.5-88.5] vs 74.3 [IQR 62.3-90.5], P = 0.613), as well as pre-LD and post-LD groups (66.6 [IQR 47.8-73.7] vs 63.9 [IQR 55.0-77.1], P = 0.600). There were no significant differences in rates of acute rejection episodes or hospitalization within 1 year of transplantation for in LD/DD groups. CONCLUSION: There is no significant difference in renal function at 1 year post-transplant in pediatric patients receiving pre-emptive or post-dialysis kidney transplants.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".