A pooled analysis of melanocytic nevus phenotype and the risk of cutaneous melanoma at different latitudes
Bibliographic record
Abstract
An abnormal nevus phenotype is associated with an increased risk of melanoma. We report a pooled analysis conducted using individual nevus data from 15 case-control studies (5,421 melanoma cases and 6,966 controls). The aims were to quantify the risk better and to determine whether relative risk is varied by latitude. Bayesian unconditional logistic random coefficients models were employed to study the risk associated with nevus characteristics. Participants with whole body nevus counts in the highest of 4 population-based categories had a greatly increased risk of melanoma compared with those in the lowest category (pooled odds ratio (pOR) 6.9 (95% confidence interval (CI): 4.4, 11.2) for those aged<50 years and pOR 5.1 (95% CI: 3.6, 7.5) for those aged>or=50). The pOR for presence compared with absence of any clinically atypical nevi was 4.0 (95% CI: 2.8, 5.8). The pORs for 1-2 and >or=3 large nevi on the body compared with none were 2.9 (95% CI: 1.9, 4.3) and 7.1 (95% CI: 4.7, 11.6), respectively. The relative heterogeneities among studies were small for most measures of nevus phenotype, except for the analysis of nevus counts on the arms, which may have been due to methodological differences among studies. The pooled analysis also suggested that an abnormal nevus phenotype is associated most with melanomas on intermittently sun-exposed sites. The presence of increased numbers of nevi, large nevi and clinically atypical nevi on the body are robust risk factors for melanoma showing little variation in relative risk among studies performed at different 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".