Skin tumours in the West of Scotland renal transplant population
Bibliographic record
Abstract
BACKGROUND: Organ transplant recipients have an increased risk of skin cancers. A specialist dermatology clinic for renal transplant recipients (RTRs) was established in 2005. OBJECTIVES: To analyse the type and incidence of skin cancers in prevalent patients in the West of Scotland after renal transplant, and to analyse the impact of the time since transplant and the immunosuppression regimen. METHODS: Skin cancer data for RTRs attending the transplant dermatology clinic over a 38-month period were collected and recorded in the West of Scotland electronic renal patient record. Skin cancer data were intrinsically linked to each individual's transplant and immunosuppression data. RESULTS: Overall, 610 patients attended. The median follow-up time from the date of first transplant was 10 years. Ninety-three patients (15.2%) had experienced a total of 368 skin cancers since transplant, and the prevalence increased with time since transplant. Basal cell carcinomas (BCCs) occurred in 74 patients (12.1%) and squamous cell carcinomas (SCCs) in 42 patients (6.9%). Three patients (0.5%) had experienced a melanoma. The SCC:BCC ratio was 0.7. Survival analysis showed significant reduction in the time to develop skin cancer in patients transplanted from 1995 onwards (P < 0.0001) and in patients who had been on triple immunosuppressant therapy at 1 year after transplant, compared with dual therapy (P < 0.0001). CONCLUSIONS: This is the first study of skin cancer in prevalent Scottish RTRs. The incidence of skin cancer is high and appears to have a direct relationship to the overall burden of immunosuppression. The SCC:BCC ratio, which is lower than reports from other centres, deserves further scrutiny.
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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 | 0.001 | 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".