Changes in Ultraviolet Radiation Exposure to the Ocular Region: A Population-Based Study
Bibliographic record
Abstract
In contrast to the well-established association between ultraviolet radiation (UVR) exposure and skin cancers, the relationship between UVR and uveal malignant melanoma (UM) remains controversial. To address this controversy, we evaluated the incidence rates of cutaneous malignancies in the eyelids as a proxy for UVR exposure in the ocular region using a population-based cancer registry. Overall, 74,053 cases of eyelid basal cell carcinoma (BCC) and 7890 cases of melanoma over a 26-year period (1982–2007) were analyzed. The incidence of eyelid basal cell carcinoma and uveal melanoma remained stable, whereas other cutaneous areas demonstrated an increase in the rates. A comparability test demonstrated that BCC incidence trends were significantly different between the eyelid versus both chronically exposed (males p = 0.001; females p = 0.01) and intermittently exposed skin (males and females, p = 0.0002), as well as the skin of the face (males p = 0.002; females p = 0.02). Similarly, melanoma trends were significantly different between the UM group versus both chronically exposed cutaneous melanoma (CM) (males p = 0.001; females p = 0.04) and intermittently exposed CM (males p = 0.005), as well as facial skin CM (males and females p = 0.0002). The discrepancy of cancer incidence between tumors in the peri-ocular region versus the rest of the body suggests that the peri-ocular region might have a different or unique exposure pattern to ultraviolet radiation.
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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 | 0.000 | 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".