MC1R variants and cutaneous melanoma risk according to histological type, body site, and Breslow thickness: a pooled analysis from the M-SKIP project
Bibliographic record
Abstract
Little is known on whether melanocortin 1 receptor (MC1R) associated cutaneous melanoma (CM) risk varies depending on histological subtype and body site, and whether tumour thickness at diagnosis (the most important prognostic factor for CM patients) differs between MC1R variant carriers and wild-type individuals. We studied the association between MC1R variants and CM risk by histological subtype, body site, and Breslow thickness, using the database of the M-SKIP project. We pooled individual data from 15 case-control studies conducted during 2005-2015 in Europe and the USA. Study-specific, multi-adjusted odds ratios were pooled into summary odds ratios (SOR) and 95% confidence intervals (CI) using random-effects models. Six thousand eight hundred ninety-one CM cases and 5555 controls were included. CM risk was increased among MC1R variant carriers vs. wild-type individuals. The increase in risk was comparable across histological subtypes (SOR for any variant vs. wild-type ranged between 1.57 and 1.70, always statistical significant) except acral lentiginous melanoma (ALM), for which no association emerged; and slightly greater on chronically (1.74, 95% CI 1.47-2.07) than intermittently (1.55, 95% CI 1.34-1.78) sun-exposed skin. CM risk was greater for those carrying 'R' vs. 'r' variants; correlated with the number of variants; and was more evident among individuals not showing the red hair colour phenotype. Breslow thickness was not associated with MC1R status. MC1R variants were associated with an increased risk of CM of any histological subtype (except ALM) and occurring on both chronically and intermittently sun-exposed skin.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".