Morphologic and Molecular Features of Antibody-Mediated Transplant Rejection: Pivotal Role of Molecular Injury as an Independent Predictor of Renal Allograft Functional Decline
Bibliographic record
Abstract
Current knowledge about the factors correlating with functional decline and subsequent failure of kidney allografts in antibody-mediated rejection (ABMR) is limited. We conducted a cohort study involving 75 renal allograft recipients diagnosed with late ABMR occurring at least 6 months after transplantation. The study aimed to examine the correlation of molecular and histologic features with estimated glomerular filtration rate (eGFR) trajectories and death-censored graft survival. We focused on sum scores reflecting histologic ABMR activity versus chronicity and molecular scores of ABMR probability (ABMRProb), injury-repair response (IRRAT) and fibrosis (ciprob). In multivariable Cox analysis, a Banff lesion-based chronicity index (ci+ct+cg[x2]; hazard ratio per interquartile range [IQR]: 1.97 [95% confidence interval: 0.97 to 3.99]) and IRRAT (1.93 [0.96 to 3.89]) showed the strongest associations with graft failure. Among biopsy variables, IRRAT exhibited the highest relative variable importance and emerged as the sole independent predictor of eGFR slope (change per IQR: −4.2 [−7.8 to −0.6] mL/min/1.73 m2/year). In contrast, morphologic chronicity associated with baseline eGFR only. We conclude that the extent of molecular injury is a robust predictor of renal function decline. Transcriptome analysis has the potential to improve outcome prediction and possibly identify modifiable injury, guiding targeted therapeutic interventions.
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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 | 0.001 |
| 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 | 0.000 |
| 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".