Prostate cancer in renal transplant recipients
Bibliographic record
Abstract
BACKGROUND: We conducted a retrospective multi-centre study to determine the characteristics of prostate cancer in renal transplant recipients (RTR) and to analyse the relation with immunosuppressive maintenance therapies. METHODS: Patients from 19 French transplant centres diagnosed with prostate cancer at least 1 year after kidney transplantation were included in this study. Data regarding demographics, kidney transplantation, prostate cancer and immunosuppressive treatment were analysed. RESULTS: Sixty-two patients met the eligibility criteria for this study. Thirty-eight patients (61.3%) received calcineurin inhibitors (CNI) and azathioprine (AZA) with or without steroids, twenty received CNI with or without steroids (32.2%) and four received CNI and mycophenolate mofetil (6.5%). Patients with CNI and AZA immunosuppressive therapy presented more high-stage cancer (T3 and T4) when compared to patients receiving CNI alone (47.5% versus 15%, respectively, P = 0.03). A non-significant increase in lymph node invasion was found in patients receiving CNI and AZA compared to patients receiving CNI alone (21% versus 5%, P = 0.16). In the multivariate analysis, the immunosuppressive regimen with CNI and AZA was the only independent risk factor for locally advanced disease (P = 0.007). CONCLUSION: Our results showed that RTR are at risk for early occurrence and for locally advanced prostate cancer, especially when they received a CNI and AZA maintenance immunosuppressive therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".