Identifying Social Determinants of Melanoma Incidence and Diagnosis in Canada
Bibliographic record
Abstract
BackgroundMelanoma is the seventh most common cancer in Canada, with incidence on the rise for both men and women.This has been largely attributed to increased exposure to ultraviolet radiation (UVR), as well as improved early detection of melanoma.Melanoma prevention and early detection are lifesaving interventions.In fact, despite increasing incidence, melanoma mortality rates are on the decline.The identification of Canadians at risk of developing melanoma is essential for policy makers and clinicians.A Statistics Canada Health Report on UVR exposure and melanoma risk within the 1991 Canadian Census Health and Environment Cohort found that overall melanoma risk was associated with higher sociodemographic characteristics of income, education, and occupation.A recent systematic review of Canadian studies on the relationship between socioeconomic status (SES) and melanoma was limited by the small number of studies, heterogeneous study design and use of different measures of socioeconomic status.Objective This single manuscript thesis investigates the relationships between sociodemographic factors and the age at diagnosis and stage at diagnosis of melanoma using Canada-wide data from the national cancer registry linked to individual responses from the Canadian Community Health Survey (CCHS).Methods This study is conducted using an existing linked dataset from Statistics Canada: Canadian Population Health Survey data (CCHS Annual and Focus Content) integrated with mortality, hospitalization, historical postal codes, cancer registry, tax files and Census data.Data files from the Canadian Cancer Registry (CCR) from 2010-2016 are linked to responses from the 2015, 2016 and 2017 CCHS cycles.By linking these datasets, I examine relationships between the age of
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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 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".