Time trends and age–period–cohort analysis of cutaneous malignant melanoma incidence rates in the Romagna Region (northern Italy), 1986–2014
Bibliographic record
Abstract
After a long-term increase, the incidence of cutaneous malignant melanoma has stabilized recently or even decreased in several populations of North-western Europe, USA, Canada, Australia and New Zealand, but not in southern Europe. The incidence trends of primary invasive cutaneous malignant melanoma (International Classification of Diseases, 10th revision, codes C43.0-C43.9) in the Romagna Region (northern Italy, 1.2 million inhabitants) for the period 1986-2014 were analysed with an age-period-cohort modelling approach. The series included 2466 men and 2481 women, a total of 4947 patients. Using the method of model building, the best-fitting models were found to be an age-drift model for men and an age-period model for women. Among men, the age-specific incidence rates increased in each successive cohort born between 1916 and 1981 with an attenuation of the trend for younger ones in the last cohorts. Among younger women, a slight decrease occurred for the cohorts born after 1961. For men, the quasi-parallel appearance of incidence curves by age group and cohort on a log scale suggested that the observed change was explained by a linear cohort effect. For women, the curves tended to overlap, suggesting an interaction between age and cohort that could be explained as a nonlinear period effect. In conclusion, the long-term upward incidence trend in the study area is stabilizing among women and an attenuation of the increasing trend is occurring among younger men in the most recent cohorts. These observations need to be confirmed with longer-term studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".