International trends in the incidence of malignant melanoma 1953–2008—are recent generations at higher or lower risk?
Bibliographic record
Abstract
The incidence of cutaneous malignant melanoma has steadily increased over the past 50 years in predominately fair-skinned populations. This increase is reported to have leveled off recently in several Northern and Western European countries, Australia, New Zealand and in North America. We studied the global patterns and time trends in incidence of melanoma by country and sex, with a focus on and age- and cohort-specific variations. We analyzed the incidence data from 39 population-based cancer registries, examining all-ages and age-truncated standardized incidence rates of melanoma, estimating the annual percentage change and incidence rate ratios from age-period-cohort models. Incidence rates of melanoma continue to rise in most European countries (primarily Southern and Eastern Europe), whereas in Australia, New Zealand, the U.S., Canada, Israel and Norway, rates have become rather stable in recent years. Indications of a stabilization or decreasing trend were observed mainly in the youngest age group (25-44 years). Rates have been rising steadily in generations born up to the end of the 1940s, followed by a stabilization or decline in rates for more recently born cohorts in Australia, New Zealand, the U.S., Canada and Norway. In addition to the birth cohort effect, there was a suggestion of a period-related influence on melanoma trends in certain populations. Although our findings provide support that primary and secondary prevention can halt and reverse the observed increasing burden of melanoma, they also indicate that those prevention measures require further endorsement in many countries.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".