Melanoma Diagnostic Processes and the Occurrence of Advanced Disease in Ontario
Bibliographic record
Abstract
Background: Reasons for the occurrence of advanced melanoma and an understanding of melanoma diagnostic processes and their relationship with advanced disease is understudied. \nMethods: This thesis examined the melanoma diagnostic process in Ontario using abstracted pathology reports and administrative data held at ICES. Objective 1 described the occurrence of advanced melanoma using abstracted pathology reports on a 65% random sample of people diagnosed with melanoma in Ontario from 2007–2012. We used modified Poisson regression to identify factors associated with advanced melanoma, stratifying by ulceration, which is a marker of clinical appearance and prognosis. Objectives 2–4 used ICES data. Objective 2 described the melanoma diagnostic interval (DI) for all Ontario melanoma patients diagnosed from 2007–2019. We used quantile regression to evaluate the association between patient-, disease-, and system-level factors and the length of the DI and primary care (PCI) and specialist care (SCI) subintervals. Objective 3 used latent class cluster analysis to group patients who experienced similar diagnostic processes. Objective 4 used quantile regression with restricted cubic splines to examine the relationship between melanoma thickness and DI length, stratified by ulceration. \nResults: Advanced melanoma was diagnosed in 26% of patients, and age, sex, socioeconomic status, and health region were associated with an increased risk of advanced disease. Ulceration attenuated these effects. Median DI was 36 days (Interquartile range [IQR]: 8–85 days), PCI was 22 days (IQR: 6–54 days) and SCI was 6 days (IQR: 1–42 days). Different factors were associated with the lengths of the DI, PCI, and SCI. We identified four diagnostic pathways: “primary care only”, “referred to specialist for immediate action”, “multiple visits and procedures in SCI”, and “specialist care only”. The distribution of patient-, disease-, and system-level factors, along with the lengths of the DI, PCI, and SCI varied across pathways. Finally, we found a complex relationship between thickness and DI duration, which differed by ulceration. \nConclusions: We found independent risk factors for advanced melanoma in Ontario. This research identified variation in the melanoma diagnostic process within, and across, Ontario. We did not find a significant association between a longer DI and thicker melanomas.
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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 | 0.003 |
| 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) | 0.003 | 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".