Disparities in diagnosis of advanced melanoma: a population-based cohort study
Bibliographic record
Abstract
Background: International studies have observed inequities in stage at diagnosis of melanoma. As this has not been sufficiently studied in Canada, the purpose of this study was to investigate whether there are disparities in the diagnosis of advanced-thickness melanoma in the province of Ontario. Methods: In this retrospective population-based cohort study, we obtained, abstracted and linked pathology reports for a 65% random sample of all cases of invasive cutaneous melanoma in Ontario from 2007 to 2012 in the Ontario Cancer Registry. Cases without pathology reports or with unreported thickness were excluded from the primary analysis. Associations between advanced melanoma (thickness > 2.0 mm) and patient, health-system and tumour factors were described and analyzed using multivariable modified Poisson regression. Results: In total, 8042 patients had histologically confirmed melanoma and thickness information. Of these, 46.7% (n = 3755) were female, the median age at diagnosis was 62 years and 25.7% (n = 2069) had advanced melanoma. In multivariate analyses, advanced age (relative risk [RR] 1.53; 95% confidence interval [CI] 1.37–1.72), male sex (RR 1.12, 95% CI 1.05–1.20), lowest socioeconomic status quintile (RR 1.24; 95% CI 1.12–1.38) and health region (RR range 0.92–1.34, p = 0.005 for variable) were significantly associated with advanced melanoma. Presence of ulceration significantly modified many of these associations. Interpretation: Disparate rates of advanced melanoma according to patient and health system factors suggest there may be inequitable access to timely diagnosis of melanoma in Ontario. This highlights a potential opportunity for system improvement to ensure timely and equitable access to melanoma care.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".