Malignancy Risk in Kidney Transplant Recipients Exposed to Immunosuppression Pre-Transplant for the Treatment of Glomerulonephritis
Bibliographic record
Abstract
Background: Kidney transplant patients with glomerulonephritis (GN) as their native disease may be exposed to significant amounts of pre-transplant immunosuppression (PTI), which could increase the risk for the development of malignancy post-transplant. Methods: We conducted a single-center, retrospective study of adult and pediatric kidney transplant recipients at University of North Carolina Hospitals from January 2005 until May 2020. Patients with GN as their native kidney disease who received PTI for the treatment of GN (n=184) were compared to a control cohort (n=579) of non-diabetic, non-PTI receiving kidney transplant patients. We calculated hazard ratios (HR) with 95% confidence intervals (95%CI) for the outcomes of the first occurrence of solid or hematologic malignancy, non-melanoma skin cancer (NMSC) and post-transplant lymphoproliferative disorder (PTLD). Results: Over a median follow-up of 5.7 years, PTI for GN was associated with significantly increased risk for malignancy compared to controls (13.0% vs 9.7% respectively, adjusted HR 1.82 [95%CI 1.10-3.00]), but not for NMSC (10.3% vs 11.4% respectively, adjusted HR 1.09 [95%CI 0.64-1.83]) nor PTLD (3.3% vs 3.1% respectively, adjusted HR 1.02 [95%CI 0.40-2.61]). The risk for malignancy was significantly increased in those who received cyclophosphamide (HR 2.59 [95%CI 1.48-4.55]) or rituximab (HR 3.82 [95%CI 1.69-8.65]) pre-transplant, and particularly in those who received both cyclophosphamide and rituximab, but not for calcineurin inhibitors nor mycophenolate. Conclusions: The use of PTI for treatment of GN, in particular cyclophosphamide or rituximab, is associated with increased risk for development of solid or hematologic malignancy post-transplant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".