Detection of Subclinical Rejection in Pediatric Kidney Transplantation: Current and Future Practices
Bibliographic record
Abstract
INTRODUCTION: The successes in the field of pediatric kidney transplantation over the past 60 years have been extraordinary. Year over year, there have been significant improvements in short-term graft survival. However, improvements in longer-term outcomes have been much less apparent. One important contributor has been the phenomenon of low-level rejection in the absence of clinical manifestations-so-called subclinical rejection (SCR). METHODS: Traditionally, rejection has been diagnosed by changes in clinical parameters, including but not limited to serum creatinine and proteinuria. This review examines the shortcomings of this approach, the effects of SCR on kidney allograft outcome, the benefits and drawbacks of surveillance biopsies to identify SCR, and new urine and blood biomarkers that define the presence or absence of SCR. RESULTS: Serum creatinine is an unreliable index of SCR. Surveillance biopsies are the method most utilized to detect SCR. However, these have significant drawbacks. New biomarkers show promise. These biomarkers include blood gene expression profiles and donor derived-cell free DNA; urine gene expression profiles; urinary cytokines, chemokines, and metabolomics; and other promising blood and urine tests. CONCLUSION: Specific emphasis is placed on studies carried out in pediatric kidney transplant recipients. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03719339.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".