HLA class, calcineurin inhibitor levels, and the risk of graft failure in kidney recipients with de novo donor-specific antibodies
Bibliographic record
Abstract
Introduction: De novo donor-specific HLA antibody (dnDSA) are associated with poor outcomes. Whether this observation applies to both HLA class I and II dnDSA remains unclear. Methods: We studied 1236 consecutive kidney recipients who had routine anti-HLA antibody surveillance post-transplant. Results: During the screening period, 55/1236 (4.4%) patients developed dnDSA: 18 (33%) HLA-I only, 33 (60%) HLA-II only, and 4 (7%) both classes. Thirty patients experienced graft loss at a median of 39 months after dnDSA detection: 9/18 (50%) HLA-I only, 17/33 (52%) HLA-II only, and 4/4 (100%) both classes. A control group was created by matching patients with dnDSA to patients who did not develop DSA and had a functioning graft at the time of dnDSA detection in their respective cases. Compared with these controls, the risk estimates of graft loss were similar between patients with HLA-I only and HLA-II only dnDSA (aHR [95% CI] 2.7 [1.1-6.6], p=0.04 and 3.1 [1.5-6.6], p<0.01 respectively). Additionally, the risk of graft loss decreased with increasing CNI trough levels following dnDSA detection (aHR 0.7 [0.6-0.9] for each increase in 1 ng/mL, p=0.02). Conclusions: The prognosis of patients with dnDSA is similar regardless of the HLA class specificity. Lower calcineurin inhibitor levels predict graft loss in such patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".