Analysis of global skin cancer epidemiology in 2022 and correlation with dermatologist density
Bibliographic record
Abstract
Dear Editor, Skin cancer represents a significant health burden, with rising incidence worldwide.1 We analysed melanoma and nonmelanoma skin cancer rates across 185 countries, examining the relationship with dermatologist density to understand healthcare system effectiveness.The International Agency for Research on Cancer (IARC) GLOBOCAN 2022 estimates provided data for melanoma (ICD-10 code C43) and nonmelanoma skin cancer (NMSC, ICD-10 code C44), with NMSC incidence excluding basal cell carcinoma while mortality includes it.2 CDC data contributed melanoma incidence by ethnicity in the United States.3 Agestandardized rates per 100,000 person-years were calculated using the World standard population, accounting for varying population age structures.4 Dermatologist density data was sourced from an unpublished L'Oreal survey covering 31 countries, with density calculated per 100,000 population.5 This survey compiled information from official registries, publications and professional associations.5 In 2022, there were 331,722 estimated melanoma cases (58,667 deaths) and 1.2 million NMSC cases (69,416 deaths) globally.The highest age-standardized incidence rates (ASR) for melanoma were found in Oceania (29.78/100,000),North America (16.3) and Europe (10.43).Notably,
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".