Rates and Determinants of Progression to Graft Failure in Kidney Allograft Recipients With De Novo Donor-Specific Antibody
Bibliographic record
Abstract
Understanding rates and determinants of clinical pathologic progression for recipients with de novo donor-specific antibody (dnDSA), especially subclinical dnDSA, may identify surrogate endpoints and inform clinical trial design. A consecutive cohort of 508 renal transplant recipients (n = 64 with dnDSA) was studied. Recipients (n = 388) without dnDSA or dysfunction had an eGFR decline of -0.65 mL/min/1.73 m(2) /year. In recipients with dnDSA, the rate eGFR decline was significantly increased prior to dnDSA onset (-2.89 vs. -0.65 mL/min/1.73 m(2) /year, p < 0.0001) and accelerated post-dnDSA (-3.63 vs. -2.89 mL/min/1.73 m(2) /year, p < 0.0001), suggesting that dnDSA is both a marker and contributor to ongoing alloimmunity. Time to 50% post-dnDSA graft loss was longer in recipients with subclinical versus a clinical dnDSA phenotype (8.3 vs. 3.3 years, p < 0.0001). Analysis of 1091 allograft biopsies found that dnDSA and time independently predicted chronic glomerulopathy (cg), but not interstitial fibrosis and tubular atrophy (IFTA). Early T cell-mediated rejection, nonadherence, and time were multivariate predictors of IFTA. Independent risk factors for post-dnDSA graft survival available prior to, or at the time of, dnDSA detection were delayed graft function, nonadherence, dnDSA mean fluorescence intensity sum score, tubulitis, and cg. Ultimately, dnDSA is part of a continuum of mixed alloimmune-mediated injury, which requires solutions targeting T and B cells.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".