Antibody-Mediated Microcirculation Injury Is the Major Cause of Late Kidney Transplant Failure
Bibliographic record
Abstract
We studied the phenotype of late kidney graft failure in a prospective study of unselected kidney transplant biopsies taken for clinical indications. We analyzed histopathology, HLA antibodies and death-censored graft survival in 234 consecutive biopsies from 173 patients, taken 6 days to 31 years posttransplant. Patients with late biopsies (>1 year) frequently displayed donor-specific HLA antibody (particularly class II) and microcirculation changes, including glomerulitis, glomerulopathy, capillaritis, capillary multilayering and C4d staining. Grafts biopsied early rarely failed (1/68), whereas grafts biopsied late often progressed to failure (27/105) within 3 years. T-cell-mediated rejection and its lesions were not associated with an increased risk of failure after biopsy. In multivariable analysis, graft failure correlated with microcirculation inflammation and scarring, but C4d staining was not significant. When microcirculation changes and HLA antibody were used to define antibody-mediated rejection, 17/27 (63%) of late kidney failures after biopsy were attributable to antibody-mediated rejection, but many were C4d negative and missed by current diagnostic criteria. Glomerulonephritis accounted for 6/27 late losses, whereas T-cell-mediated rejection, drug toxicity and unexplained scarring were uncommon. The major cause of late kidney transplant failure is antibody-mediated microcirculation injury, but detection of this phenotype requires new diagnostic criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".