Nevus density and melanoma risk in women: A pooled analysis to test the divergent pathway hypothesis
Bibliographic record
Abstract
A "divergent pathway" model for the development of cutaneous melanoma has been proposed. The model hypothesizes that melanomas occurring in people with a low tendency to develop nevi will, on average, arise more commonly on habitually sun-exposed body sites such as the head and neck. In contrast, people with an inherent propensity to develop nevi will tend to develop melanomas most often on body sites with large melanocyte populations, such as on the back. We conducted a collaborative analysis to test this hypothesis using the original data from 10 case-control studies of melanoma in women (2,406 cases and 3,119 controls), with assessment of the potential confounding effects of socioeconomic, pigmentary and sun exposure-related factors. Higher nevus count on the arm was associated specifically with an increased risk of melanoma of the trunk (p for trend = 0.0004) and limbs (both upper and lower limb p for trends = 0.01), but not of the head and neck (p for trend = 0.25). The pooled odds ratios for the highest quartile of nonzero nevus count versus none were 4.6 (95% confidence interval (CI) 2.7-7.6) for melanoma of the trunk, 2.0 (95% CI 0.9-4.5) for the head and neck, 4.2 (95% CI 2.3-7.5) for the upper limbs and 3.4 (95% CI 1.5-7.9) for the lower limbs. Aggregate data from these studies suggest that high nevus counts are strongly associated with melanoma of the trunk but less so if at all of the head and neck. This finding supports different etiologic pathways of melanoma development by anatomic site.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".