Mortality from cutaneous melanoma: evidence for contrasting trends between populations
Bibliographic record
Abstract
In recent years several reports have been published concerning trends in melanoma mortality in different countries, some of which have indicated that rates are beginning to fall. Many of these reports, however, have been based on small populations and have used different forms of statistical analysis. Our objective was to analyse systematically to what degree the epidemic of melanoma mortality had evolved similarly in different populations and whether there were any divergent trends that might increase our understanding. Instead of using all available data, we focused on countries with a minimum time series of 30 years and a minimum of 100 deaths annually in at least one sex from melanoma. We first inspected sex-specific age-standardized mortality rates and then performed age-period-cohort modelling. We found that the increase in mortality observed after 1950 was more pronounced in the age group 60-79. Statistical modelling showed a general increase in mortality rates in generations born after the turn of the century. Downturns in mortality, essentially in women and starting with generations born just before World War II, were found in Australia (where the earliest decreases were noted), the Nordic countries and the USA. Small decreases in rates in more recent generations were found in the UK and Canada. However, in France, Italy and Czechoslovakia, mortality rates were seen to be still increasing in recent cohorts. Our analysis suggests that populations are at different places on the melanoma mortality epidemic curve. The three trend patterns we observed are in agreement with time differences between populations with respect to the promotion of sun protection and the surveillance of pigmented skin lesions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".