Risk of lip cancer after solid organ transplantation in the United States
Bibliographic record
Abstract
Solid organ transplant recipients have an increased risk of lip cancer, but the reasons are uncertain. Using data from the Transplant Cancer Match Study, we describe the epidemiology of lip cancer among 261 500 transplant recipients in the United States. Two hundred thirty-one lip cancers were identified, corresponding to elevated risks for both invasive and in situ lip cancers (standardized incidence ratios of 15.3 and 26.2, respectively). Invasive lip cancer incidence was associated with male sex (adjusted incidence rate ratio [aIRR] 2.01, 95% CI 1.44-2.82), transplanted organ (0.33, 0.20-0.57, for liver transplants and 3.07, 1.96-4.81, for lung transplants, compared with kidney transplants), and racial/ethnic groups other than non-Hispanic whites (0.09, 0.04-0.2). In addition, incidence increased with age and during the first 3 years following transplant, and was higher in recipients prescribed cyclosporine/azathioprine maintenance therapy (aIRR 1.79, 95% CI 1.09-2.93, compared with use of tacrolimus/mycophenolate mofetil) and following a diagnosis of cutaneous squamous cell carcinoma (4.21, 2.69-0.94). The elevation in lip cancer incidence is consistent with an effect of immunosuppression. Notably, the very strong associations with white race and history of prior skin cancer point to an important role for ultraviolet radiation exposure, and cyclosporine and azathioprine may contribute as photosensitizing or DNA damaging agents.
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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 | 0.001 | 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".