Non-Melanoma Skin Cancer Incidence and Risk Factors After Kidney Transplantation: A Canadian Experience
Bibliographic record
Abstract
BACKGROUND: Non-melanoma skin cancer (NMSC) after kidney transplantation is common and can result in significant morbidity and mortality. Incidence and risk factors for NMSC can vary between geographic locations and there is no literature describing the incidence or risk factors for NMSC in Canada. METHODS: The purpose of this retrospective cohort study was to determine the incidence of NMSC, the time of development of NMSC, and risk factors (including sun exposure history) for NMSC in kidney transplant recipients between 1990 and 2003 in our center (n=926). RESULTS: We observed a 9.7% incidence of NMSC lesions after kidney transplant with a median time of development of a first NMSC lesion of 4 years. Risk factors for NMSC (multivariate analysis) include older men (>45 years), a history of posttransplant warts, and longer duration of residence in a northern climate. CONCLUSION: We conclude that NMSC is common after kidney transplantation in a northern climate and these individuals require disease prevention-specific education, more vigilant surveillance and early referral and treatment.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".