Melanoma in adolescents: A case‐control study of risk factors in Queensland, Australia
Bibliographic record
Abstract
The incidence of melanoma increases markedly in the second decade of life but almost nothing is known of the causes of melanoma in this age group. We report on the first population-based case-control study of risk factors for melanoma in adolescents (15-19 years). Data were collected through personal interviews with cases, controls and parents. A single examiner conducted full-body nevus counts and blood samples were collected from cases for analysis of the CDKN2A melanoma predisposition gene. A total of 201 (80%) of the 250 adolescents with melanoma diagnosed between 1987 and 1994 and registered with the Queensland Cancer Registry and 205 (79%) of 258 age-, gender- and location-matched controls who were contacted agreed to participate. The strongest risk factor associated with melanoma in adolescents in a multivariate model was the presence of more than 100 nevi 2 mm or more in diameter (odds ratio [OR] = 46.5, 95% confidence interval [CI] = 11.4-190.8). Other risk factors were red hair (OR = 5.4, 95%CI = 1.0-28.4); blue eyes (OR = 4.5, 95%CI = 1.5-13.6); inability to tan after prolonged sun exposure (OR = 4.7, 95%CI = 0.9-24.6); heavy facial freckling (OR = 3.2, 95% CI = 0.9-12.3); and family history of melanoma (OR = 4.0, 95%CI = 0.8-18.9). Only 2 of 147 cases tested had germline variants or mutations in CDKN2A. There was no association with sunscreen use overall, however, never/rare use of sunscreen at home under the age of 5 years was associated with increased risk (OR = 2.2, 95%CI = 0.7-7.1). There was no difference between cases and controls in cumulative sun exposure in this high-exposure environment. Factors indicating genetic susceptibility to melanoma, in particular, the propensity to develop nevi and freckles, red hair, blue eyes, inability to tan and a family history of the disease are the primary determinants of melanoma among adolescents in this high solar radiation environment. Lack of association with reported sun exposure is consistent with the high genetic susceptibility in this group.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".