Donor‐specific anti‐<scp>HLA</scp> antibodies in pediatric renal transplant recipients with creeping creatinine: Prevalence, histological correlations, and impact on patient and graft survival
Bibliographic record
Abstract
Donor-specific anti-HLA antibodies (DSA) causing CAMR are responsible for a high proportion of long-term graft failures after RTX. We studied the prevalence of DSA in RTX children biopsied for creeping Cr, its relationship with NA, and patient and graft survival according to histopathology. Between 2008 and 2013, 92 children were biopsied at a median of 38 months post-RTX. At biopsy, the prevalence of DSA was 49% and C4d 70%. NA rate was 45%, higher in adolescents (60%). Most frequent diagnoses were CAMR (72%) and interstitial fibrosis with tubular atrophy (IFTA) (28%). Forty-five of 66 patients with CAMR (68%) had detectable DSA. Twenty-one DSA-negative patients with CAMR had histological damage (IFTA + C4d positivity). C4d was detected in 64 of 66 biopsies with CAMR. Recipients with IFTA alone had neither C4d, nor detectable DSA, and were adherent. Graft survival at five yr was 89% in patients with CAMR, 79% in those with CAMR + TCMR Banff I, 33% in those with CAMR + TCMR Banff II, and 96% in those with IFTA. ABMR and complement activation were frequent in children biopsied for creeping Cr. Recipients with DSA were more likely to be non-adherent and have CAMR or CAMR + TCMR and worse graft survival.
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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 | 0.001 |
| 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 | 0.000 | 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".