Biologic markers of sun exposure and melanoma risk in women: Pooled case–control analysis
Bibliographic record
Abstract
A model has been proposed whereby melanomas arise through two distinct pathways dependent on the relative influence of host susceptibility and sun exposure. Such pathways may explain site-specific patterns of melanoma occurrence. To explore this model, we investigated the relationship between melanoma risk and general markers of acute (recalled sunburns) and chronic (prevalent solar keratoses) sun exposure, stratified by anatomic site and host phenotype. Our working hypothesis was that head and neck melanomas have stronger associations with solar keratoses and weaker associations with sunburn than trunk melanomas. We conducted a collaborative analysis using original data from women subjects of 11 case-control studies of melanoma (2,575 cases, 3,241 controls). We adjusted for potential confounding effects of sunlamp use and sunbathing. The magnitude of sunburn associations did not differ significantly by melanoma site, nevus count or histologic subtype of melanoma. Across all sites, relative risk of melanoma increased with an increasing number of reported lifetime "painful" sunburns, lifetime "severe" sunburns and "severe" sunburns in youth (p(trend) < 0.001), with pooled odds ratios (pORs) for the highest category of sunburns versus no sunburns of 3.22 [95% confidence interval (CI) 2.04-5.09] for lifetime "painful" sunburns, 2.10 (95%CI 1.30-3.38) for lifetime "severe" sunburns and 2.43 (95%CI 1.61-3.65) for "severe" sunburns in youth. Solar keratoses strongly increased the risk of head and neck melanoma (pOR 4.91, 95%CI 2.10-11.46), but data were insufficient to assess risk for other sites. Reported sunburn is strongly associated with melanoma on all major body sites.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".