Follow‐up biopsies identify high rates of persistent rejection in pediatric kidney transplant recipients after treatment of T cell‐mediated rejection
Bibliographic record
Abstract
Abstract Background Incomplete resolution of T cell‐mediated rejection (TCMR) after treatment may not be detected with serum creatinine monitoring and is associated with donor‐specific antibodies and chronic rejection. We evaluate the utility of follow‐up biopsies (FUB) to identify and characterize rates of persistent TCMR after treatment in pediatric kidney transplant patients. Methods Patients from two pediatric transplant centers performing standard of care FUB at 1.5–2 months after treatment for TCMR were included. FUB were evaluated for extent of rejection resolution (complete vs. incomplete) and grade. Clinical data at time of FUB and later were reported, where available. Results Fifty‐eight patients underwent FUB, at mean of 1.7 months (SD 0.7) post‐index biopsy. Rejection grade on index biopsy was Banff borderline (≥i1t1 and p = .033). Incomplete resolution of rejection on FUB was re‐treated in 25 (78%) of cases. Change in eGFR from index to FUB did not differ between those with complete and incomplete resolution (5.7 ± 32.2 vs. 13.1 ± 51.3, p = .28) and was not a sensitive marker of identifying persistent rejection. Conclusions FUB were effective at detecting persistent rejection, which was common among pediatric transplant patients after standard TCMR treatment. Until more effective rejection treatments or sensitive biomarkers are available, FUB may be effectively utilized to identify patients with ongoing rejection who would benefit from further treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".