Evolution and Clinical Pathologic Correlations of De Novo Donor-Specific HLA Antibody Post Kidney Transplant
Bibliographic record
Abstract
The natural history for patients with de novo donor-specific antibodies (dnDSA) and the risk factors for its development have not been well defined. Furthermore, clinical and histologic correlation with serologic data is limited. We studied 315 consecutive renal transplants without pretransplant DSA, with a mean follow-up of 6.2 ± 2.9 years. Protocol (n = 215) and for cause (n = 163) biopsies were analyzed. Solid phase assays were used to screen for dnDSA posttransplant. A total of 47 out of 315 (15%) patients developed dnDSA at a mean of 4.6 ± 3.0 years posttransplant. Independent predictors of dnDSA were HLA-DRβ1 MM > 0 (OR 5.66, p < 0.006); and nonadherence (OR 8.75, p < 0.001); with a strong trend toward clinical rejection episodes preceding dnDSA (OR 1.57 per rejection episode, p = 0.061). The median 10-year graft survival for those with dnDSA was lower than the No dnDSA group (57% vs. 96%, p < 0.0001). Pathology consistent with antibody-mediated injury can occur and progress in patients with dnDSA in the absence of graft dysfunction and furthermore, nonadherence and cellular rejection contribute to dnDSA development and progression to graft loss.
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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 | 0.002 |
| 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 | 0.000 |
| 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".