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 receive significant amounts of pre-transplant immunosuppression (PTI), which could increase the risk for development of malignancy post-transplant. METHODS: We conducted a single-center, retrospective study of kidney transplant recipients from January 2005 until May 2020. Patients with GN as their native kidney disease who received PTI for treatment of GN (n = 184) were compared with 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 outcomes of 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 with 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)] or 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 or mycophenolate. CONCLUSION: The use of PTI for treatment of GN, especially cyclophosphamide or even with rituximab, is associated with increased risk for development of solid or hematologic malignancy post-transplant. These data highlight potential risks with treatment of GN and underscore the importance of post-transplant malignancy surveillance in this patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".