Associations between ocular melanoma and other primary cancers: An international population‐based study
Bibliographic record
Abstract
Ocular melanoma is a rare neoplasm with a poorly understood etiology, especially concerning its link with ultraviolet-light exposure. Studying the risk of second primary cancers may help to formulate causal hypotheses. We used data from 13 cancer registries, including 10,396 first occurring ocular melanoma cases, and 404 second occurring cases. To compare the second cancer incidence in ocular melanoma patients to that in noncancer population, we calculated standardized incidence ratios (SIRs) of 32 types of cancer. We also calculated SIRs of second ocular melanoma after other primaries. Ocular melanoma patients had significantly increased risk of cutaneous melanoma (SIR = 2.38, 95% CI 1.77-3.14), multiple myeloma (SIR = 2.00, 1.29-2.95), and of liver (SIR = 3.89, 2.66-5.49), kidney (SIR = 1.70, 1.22-2.31), pancreas (SIR = 1.58, 1.16-2.11), prostate (SIR = 1.31, 1.11-1.54), and stomach (SIR = 1.33, 1.03-1.68) cancers. Risks of cutaneous melanoma were highly variable between registries and were mainly increased in females, in younger patients, in first years following diagnosis, and for patients diagnosed after 1980. The risk of ocular melanoma was significantly increased only after prostate cancer (SIR = 1.41, 1.08-1.82). Risk of cutaneous melanoma after ocular melanoma had epidemiological patterns, similar to cutaneous melanoma screening in the general population. The increased risk of cutaneous melanoma would be largely due to greater skin cancer surveillance in ocular melanoma patients, and not to common etiological factors. The high SIR found for liver cancer may be explained by misclassification bias. Common etiological factors may be involved in ocular and prostate cancers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".