Probability, Predictors, and Prognosis of Posttransplantation Glomerulonephritis
Bibliographic record
Abstract
Glomerulonephritis (GN) is the leading cause of chronic kidney disease among recipients of renal transplants. Because modern immunosuppressive regimens have reduced the incidence of rejection-related graft loss, the probability and clinical significance of posttransplantation GN (PTGN) requires reevaluation. In this Canadian epidemiologic study, we monitored 2026 sequential renal transplant recipients whose original renal disease resulted from biopsy-proven GN (36%), from presumed GN (7.8%), or from disorders other than GN (56%) for 15 yr without loss to follow-up. Kaplan-Meier estimates of PTGN in the whole population were 5.5% at 5 yr, 10.1% at 10 yr, and 15.7% at 15 yr. PTGN was diagnosed in 24.3% of patients whose original renal disease resulted from biopsy-proven GN, compared with 11.8% of those with presumed GN and 10.5% of those with disorders other than GN. Biopsy-proven GN in the native kidney, male gender, younger age, and nonwhite ethnicity predicted PTGN. Current immunosuppressive regimens did not associate with a reduced frequency of PTGN. Patients who developed PTGN had significantly reduced graft survival (10.2 versus 69.7%; P < 0.0001). In summary, in the Canadian population, PTGN is a common and serious complication that causes accelerated graft failure, despite the use of modern immunosuppressive regimens.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".