Socioeconomic Status and Melanoma in Canada: A Systematic Review
Bibliographic record
Abstract
As melanoma is one of the leading cancers in average years of life lost per death from disease, screening and early diagnosis are imperative to decrease morbidity and mortality. Socioeconomic status (SES) has been shown to be associated with melanoma incidence. However, it is unclear if this association holds true in universal healthcare systems where screening, diagnostic, and treatment services are available to all patients. The objective of this systematic review was to evaluate the evidence on the association of SES and melanoma incidence in Canada. A comprehensive search of PubMed and EMBASE yielded 7 studies reporting on melanoma incidence or outcomes with respect to SES in Canada. High SES was associated with increased melanoma incidence across all studies, which encompassed all Canadian provinces, and time periods spanning from 1979 to 2012. Studies also reported an increasing incidence of melanoma over time. There were substantial discrepancies in melanoma incidence across Canadian provinces, after controlling for SES and demographic characteristics. Populations of lower SES and living within certain healthcare regions had increased risks of advanced melanoma at diagnosis. This review highlights the potential for inequities in access to care even within a universal healthcare system. Future research is needed to characterize specific risk factors within different patient groups and within the universal health system context in order to implement targeted strategies to lower melanoma incidence, morbidity, and mortality.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".