A rational approach to guide cost-effective de novo donor-specific antibody surveillance with tacrolimus immunosuppression
Bibliographic record
Abstract
De novo donor-specific antibody (dnDSA) after renal transplantation has been shown to correlate with antibody-mediated rejection and allograft loss. However, the lack of proven interventions and the time and cost associated with annual screening for dnDSA are difficult to justify for all recipients. We studied a well-characterized consecutive cohort (n = 949) with over 15 years of prospective dnDSA surveillance to identify risk factors that would help institute a resource-responsible surveillance strategy. Younger recipient age and HLA-DR/DQ molecular mismatch were independent predictors of dnDSA development. Combining both risk factors into recipient age molecular mismatch categories, we found that 52% of recipients could be categorized as low-risk for dnDSA development (median subclinical dnDSA-free survival at 5 and 10 years, 98% and 97%, respectively). After adjustment, multivariate correlates of dnDSA development included tacrolimus versus cyclosporin maintenance immunosuppression (hazard ratio [HR], 0.37; 95% CI, 0.2-0.6; P < .0001) and recipient age molecular mismatch category: intermediate versus low (HR, 2.48; 95% CI, 1.5-4.2; P = .0007), high versus intermediate (HR, 2.56; 95% CI, 1.6-4.2; P = .0002), and high versus low (HR, 6.36; 95% CI, 3.7-10.8; P < .00001). When combined, recipient age and HLA-DR/DQ molecular mismatch provide a novel data-driven approach to reduce testing by >50% while selecting those most likely to benefit from dnDSA surveillance.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".