Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: Importance of parenchymal injury but not disease activity
Bibliographic record
Abstract
We studied the relative association of clinical, histologic, and molecular variables with risk of kidney transplant failure after an indication biopsy, both in all kidneys and in kidneys with pure antibody-mediated rejection (ABMR). From a prospective study of 1679 biopsies with histologic and molecular testing, we selected one random biopsy per patient (N = 1120), including 321 with pure molecular ABMR. Diagnoses were associated with actuarial survival differences but not good predictions. Therefore we concentrated on clinical (estimated GFR [eGFR], proteinuria, time posttransplant, donor-specific antibody [DSA]) and molecular and histologic features reflecting injury (acute kidney injury [AKI] and atrophy-fibrosis [chronic kidney disease (CKD)] and rejection. For all biopsies, univariate analysis found that failure was strongly associated with low eGFR, AKI, CKD, and glomerular deterioration, but not with rejection activity. In molecular ABMR, the findings were similar: Molecular and histologic activity and DSA were not important compared with injury. Survival in DSA-negative and DSA-positive molecular ABMR was similar. Multivariate survival analysis confirmed the dominance of molecular AKI, CKD, and eGFR. Thus, at indication biopsy, the dominant predictors of failure, both in all kidneys and in ABMR, were related to molecular AKI and CKD and to eGFR, not rejection activity, presumably because rejection confers risk via injury.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".