Routine Doppler Ultrasonography Does Not Differentiate Acute Tubular Necrosis and Rejection in Early Pediatric Kidney Transplantation
Bibliographic record
Abstract
INTRODUCTION: Differentiating acute tubular necrosis (ATN) from rejection in pediatric kidney transplant (KT) recipients remains challenging and necessitates invasive biopsy. Doppler ultrasound-derived resistive index (RI) is a noninvasive modality to assess graft status, but its diagnostic utility in children is unclear. This study evaluates RI's ability to distinguish ATN and rejection in KT. METHODS: In this retrospective cohort (2000-2021), 296 pediatric KT recipients with surveillance or clinically indicated biopsies were categorized into uncomplicated (n = 164), ATN (n = 65), or rejection (n = 67) groups. RI was measured at 24 h, 3, 6, and 12 months post-KT. Linear mixed-effects models assessed temporal trends and associations with complications. RESULTS: Baseline demographics were similar between groups (p > 0.05), but significant differences were observed in cold ischemia time (p = 0.019), time to complication (p < 0.001), and lower graft function in complicated groups (p < 0.001 and p = 0.002). Median RI did not differ between groups in surveillance (p > 0.05) or clinically indicated biopsies (p > 0.05). Established RI thresholds of 0.7 and 0.8 lacked specificity (p > 0.05). In a multivariate model, RI increased temporally posttransplant (3 months: +0.04, p < 0.001; 1 year: +0.05, p < 0.001), inversely correlated with recipient age (p < 0.001), and marginally with donor kidney size (p = 0.009), but showed no association with complications (p > 0.05). CONCLUSION: RI thresholds and trends do not differentiate ATN and rejection in pediatric KT. Temporal RI rise likely reflects systemic hemodynamic adaptation rather than pathology, limiting its standalone diagnostic utility. Future studies should integrate multimodal approaches with RI, clinical, and biochemical markers to refine noninvasive strategies.
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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 | 0.009 |
| 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 | 0.000 |
| 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".