Yield and utility of surveillance kidney biopsies in pediatric kidney transplant recipients at various time points post‐transplant
Bibliographic record
Abstract
BACKGROUND: Due to a lack of consensus on SB for pediatric kidney transplant recipients, we evaluated the yield and clinical utility of SB findings at various time points post-transplant. METHODS: Patients transplanted at a single institution between 2014 and 2020 with at least one SB at 1.5, 3, 6, 12, and 24 months post-transplant were included. Additional biopsies were done for indication (IB). TCMR was classified by Banff criteria (score ≥i1t1). RESULTS: Forty-seven patients had 142 biopsies (SB = 113, IB = 29); 19 (40.4%) of whom experienced at least one TCMR episode in the first-year post-transplant. The greatest SB yield of any pathologic abnormality was at 6 months (57.1%; P < .001). Six months also had the highest yield for TCMR (42.9%), compared with 3.3%, 20.8%, 15.0%, and 9.1% at 1.5, 3, 12 months, and 24 months, respectively (P = .003). SB instigated intensification of immunosuppression (28.3% cases), reduction of immunosuppression (2.7% cases), and other non-immunosuppressant changes (1.8% cases). The 6-month SB led to the greatest number of changes in management (53.6%), compared with 1.5, 3, 12, and 24 months (13.3, 20.8, 25.0, and 36.4%, respectively; P = .012). There were no major biopsy-related complications. CONCLUSIONS: SBs identify an important burden of subclinical rejection and other pathology leading to changes in clinical management. The greatest yield was at 6 months, whereas the least utility was at the 1.5 months. Selection of SB timing may be tailored such that the optimal yield is balanced against the procedural risk.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".