Cutaneous Melanoma Mortality-to-Incidence Ratio and Its Association With Socioeconomic and Healthcare Factors in Canada: A National Ecological Study
Bibliographic record
Abstract
INTRODUCTION: The mortality-to-incidence ratio (MIR) can be used to approximate healthcare inequities and is helpful to understand/compare cancer survival between geographic regions/jurisdictions. We investigated cutaneous melanoma (CM) outcomes through MIR analysis in Canadian jurisdictions and census divisions (CDs) between 1992 and 2016. METHODS: Data were obtained from the national databases from 1992 to 2016 for all Canadian jurisdictions, except Quebec. Age-standardized overall and median MIRs were calculated per province per year, while crude MIRs were calculated for CDs. Generalized linear regression models were conducted to study the effect of province and year on MIR, while a mixed effect regression model was used to determine how healthcare and socioeconomic factors affect MIR, while accounting for possible clustering effects (eg, year and province). RESULTS: value < .0001). The national median MIR was 15.4 (ie, 0.154 × 100), whereby Manitoba (19.9), Ontario (19.5), Saskatchewan (18.5), British Columbia (16.1), and Newfoundland and Labrador (15.9) demonstrated higher MIRs than the Canadian average. CDs with the highest MIRs were commonly identified in the southern regions of provinces. No healthcare or socioeconomic factors were found to be significantly associated with higher MIR at the provincial level. CONCLUSION: MIRs have decreased at the national and provincial levels in recent decades, which is reassuring. Higher MIRs were noted in select rural CDs and in the Canadian territories, reinforcing the importance of proper dermatological care in all parts of the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".