Association of Known Melanoma Risk Factors with Primary Melanoma of the Scalp and Neck
Bibliographic record
Abstract
Abstract Background: Scalp and neck (SN) melanoma confers a worse prognosis than melanoma of other sites but little is known about its determinants. We aimed to identify associations between SN melanoma and known risk genes, phenotypic traits, and sun exposure patterns. Methods: Participants were cases from the Western Australian Melanoma Health Study (n = 1,200) and the Genes, Environment, and Melanoma Study (n = 3,280). Associations between risk factors and SN melanoma, compared with truncal and arm/leg melanoma, were investigated using binomial logistic regression. Facial melanoma was also compared with the trunk and extremities, to evaluate whether associations were subregion specific, or reflective of the whole head/neck region. Results: Compared with other sites, increased odds of SN and facial melanoma were observed in older individuals [SN: OR = 1.28, 95% confidence interval (CI) = 0.92–1.80, Ptrend = 0.016; Face: OR = 4.57, 95% CI = 3.34–6.35, Ptrend < 0.001] and those carrying IRF4-rs12203592*T (SN: OR = 1.35, 95% CI = 1.12–1.63, Ptrend = 0.002; Face: OR = 1.29, 95% CI = 1.10–1.50, Ptrend = 0.001). Decreased odds were observed for females (SN: OR = 0.49, 95% CI = 0.37–0.64, P < 0.001; Face: OR = 0.66, 95% CI = 0.53–0.82, P < 0.001) and the presence of nevi (SN: OR = 0.66, 95% CI = 0.49–0.89, P = 0.006; Face: OR = 0.65, 95% CI = 0.52–0.83, P < 0.001). Conclusions: Differences observed between SN melanoma and other sites were also observed for facial melanoma. Factors previously associated with the broader head and neck region, notably older age, may be driven by the facial subregion. A novel finding was the association of IRF4-rs12203592 with both SN and facial melanoma. Impact: Understanding the epidemiology of site-specific melanoma will enable tailored strategies for risk factor reduction and site-specific screening campaigns.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".