Cutaneous Melanoma in Alpine Population: Incidence Trends and Clinicopathological Profile
Bibliographic record
Abstract
Previous studies associated high-level exposure to ultraviolet radiation with a greater risk of cutaneous malignant melanoma (CMM). This study focuses on the changing incidence of CMM over time (from 1990 to 2017) in the Veneto region of Northeast Italy, and its Alpine area (the province of Belluno). The clinicopathological profile of CMM by residence is also considered. A joinpoint regression analysis was performed to identify significant changes in the yearly incidence of CMM by sex and age. For each trend, the average annual percent change (AAPC) was also calculated. In the 2017 CMM cohort, the study includes a descriptive analysis of the disease's categorical clinicopathological variables. In the population investigated, the incidence of CMM has increased significantly over the last 30 years. The AAPC in the incidence of CMM was significantly higher among Alpine residents aged 0-49 than for the rest of the region's population (males: 6.9 versus 2.4; females 7.7 versus 2.7, respectively). Among the Alpine residents, the AAPC was 3.35 times greater for females aged 0-49 than for people aged 50+. The clinicopathological profile of CMM was significantly associated with the place of residence. Over three decades, the Veneto population has observed a significant increase in the incidence of CMM, and its AAPC. Both trends have been markedly more pronounced among Alpine residents, particularly younger females. While epidemiology and clinicopathological profiles support the role of UV radiation in CMM, the young age of this CMM-affected female population points to other possible host-related etiological factors. These findings also confirm the importance of primary and secondary prevention strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".