Transplant Glomerulopathy, Late Antibody-Mediated Rejection and the ABCD Tetrad in Kidney Allograft Biopsies for Cause
Bibliographic record
Abstract
To define the relative frequency of phenotypes of transplant glomerulopathy, we retrospectively reviewed the findings in 1036 biopsies for clinical indications from 1320 renal transplant patients followed in our clinics between 1997 and 2005. Transplant glomerulopathy, defined by double contours of glomerular basement membranes (D), was diagnosed in 53 biopsies (5.1%) from 41 patients (3.1%) at a median of 5.5 years post-transplant (range 3.8-381 months). In cases with D, we studied the frequency of circulating anti-HLA alloantibody (A), peritubular capillary basement membrane multilayering (B) and peritubular capillary C4d deposition (C). B was present in 48 (91%) of D biopsies. C4d staining by indirect immunofluorescence was detected in 18 of 50 D biopsies studied (36%). By Flow PRA Screening or ELISA, A was detected in 33 (70%) in 47 D cases with available sera, of which 28/33 or 85% were donor-specific. Class II (13/33) or class I and II (17/33) were more common than class I (3/33) antibodies. Thus 73% of transplant glomerulopathy has evidence of alloantibody-mediated injury (A and/or C), with ABCD and ABD being the common phenotypes in biopsies for cause. The remaining 27%, mostly BD, may be a different disease or a stage in which A and C are undetectable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".