Association between transplant glomerulopathy and graft outcomes following kidney transplantation: A meta-analysis
Bibliographic record
Abstract
Transplant glomerulopathy (TG), a morphological lesion associated with confluent mechanisms of endothelial injury of renal allografts, may provide a viable predictor of graft failure. This systematic literature review and meta-analysis were performed according to the PRISMA statement to examine evidence describing the association between TG and graft loss or failure and time to these events. The literature review was conducted using the Scopus, EBSCO, and Cochrane Library search engines. Hazard ratios, median survival times, and 95% confidence intervals (CIs) were estimated to evaluate graft survival in the total population and prespecified subgroups. Meta-regression analysis assessed heterogeneity. Twenty-one publications comprising 6,783 patients were eligible for data extraction and inclusion in the meta-analysis. Studies were highly heterogeneous (I2 = 67.3%). The combined hazard ratio of graft loss or failure from random-effects meta-analysis was 3.11 (95% CI 2.44-3.96) in patients with TG compared with those without. Median graft survival in patients with TG was 3.25 (95% CI 0.94-11.21) years-15 years shorter than in those without TG (18.82 [95% CI 10.03-35.32] years). The effect of time from transplantation to biopsy on graft outcomes did not reach statistical significance (p = 0.116). TG was associated with a threefold increase in the risk of graft loss or failure and a 15-year loss in graft survival, indicating viability as a surrogate measure for both clinical practice and studies designed to prevent or reverse antibody-mediated rejection.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".