Brief Report: Increase in Melanoma Incidence in Ontario
Bibliographic record
Abstract
BACKGROUND: Melanoma is a serious, potentially lethal disease. It is one of very few common cancers whose incidence is rising in North America. OBJECTIVES: The objective of this study was to examine trends in melanoma incidence in Ontario, Canada's most populous province, over the past 20 years. METHODS: Using data from the Ontario Cancer Registry (OCR), this retrospective cohort examined all incident cases of melanoma in Ontario from 1990 to 2012. Generalized linear modeling was used to evaluate changes in melanoma incidence over time, adjusting for age and sex using direct standardization with the 1991 Canadian census population. Tests for trend for changes in the distribution of cases by age, sex, socioeconomic status, and rurality status were also calculated. RESULTS: Our results show a statistically significant increasing incidence of melanoma in Ontario from 9.3 cases per 100 000 in 1990 to 18.0 cases per 100 000 in 2012 ( P for trend <.001, adjusted for age and sex). Incidence rates show stabilization from 2010 to 2012. CONCLUSION: Our study reveals a marked increase in melanoma incidence in Ontario, more than doubling over the past 20 years but with a stabilization more recently. Adequate availability of dermatology services may be important to ensure satisfactory care for the increased caseload and to ensure that cases may detected at an early stage with a good prognosis.
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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 | 0.001 | 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) | 0.004 | 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".