Sun exposure and melanoma risk at different latitudes: a pooled analysis of 5700 cases and 7216 controls
Bibliographic record
Abstract
BACKGROUND: Melanoma risk is related to sun exposure; we have investigated risk variation by tumour site and latitude. METHODS: We performed a pooled analysis of 15 case-control studies (5700 melanoma cases and 7216 controls), correlating patterns of sun exposure, sunburn and solar keratoses (three studies) with melanoma risk. Pooled odds ratios (pORs) and 95% Bayesian confidence intervals (CIs) were estimated using Bayesian unconditional polytomous logistic random-coefficients models. RESULTS: Recreational sun exposure was a risk factor for melanoma on the trunk (pOR = 1.7; 95% CI: 1.4-2.2) and limbs (pOR = 1.4; 95% CI: 1.1-1.7), but not head and neck (pOR = 1.1; 95% CI: 0.8-1.4), across latitudes. Occupational sun exposure was associated with risk of melanoma on the head and neck at low latitudes (pOR = 1.7; 95% CI: 1.0-3.0). Total sun exposure was associated with increased risk of melanoma on the limbs at low latitudes (pOR = 1.5; 95% CI: 1.0-2.2), but not at other body sites or other latitudes. The pORs for sunburn in childhood were 1.5 (95% CI: 1.3-1.7), 1.5 (95% CI: 1.3-1.7) and 1.4 (95% CI: 1.1-1.7) for melanoma on the trunk, limbs, and head and neck, respectively, showing little variation across latitudes. The presence of head and neck solar keratoses was associated with increased risk of melanoma on the head and neck (pOR = 4.0; 95% CI: 1.7-9.1) and limbs (pOR = 4.0; 95% CI: 1.9-8.4). CONCLUSION: Melanoma risk at different body sites is associated with different amounts and patterns of sun exposure. Recreational sun exposure and sunburn are strong predictors of melanoma at all latitudes, whereas measures of occupational and total sun exposure appear to predict melanoma predominately at low latitudes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".