Characterising melanoma diagnostic pathways for patients in routine practice using administrative health data in Ontario, Canada: a population-based study
Bibliographic record
Abstract
OBJECTIVE: To characterise diagnostic pathways for patients with melanoma in routine practice and compare patient, disease and diagnostic interval (DI) characteristics across pathways. DESIGN: Descriptive cross-sectional study using administrative health data. SETTING: Population-based study in Ontario, Canada. PARTICIPANTS: Patients with melanoma diagnosed from 2007 to 2019. MAIN OUTCOME MEASURES: tests and Pearson residuals were used. We characterised clusters by the lengths of their DI, primary care subinterval and specialist care subinterval. RESULTS: There were 33 371 patients diagnosed with melanoma from 2007 to 2019. We identified four diagnostic pathways: 'primary care only' (n=6107), 'referred to specialist with immediate action' (n=8987), 'multiple visits and procedures in specialist care' (n=11 893) and 'specialist care only' (n=6384). Patient, disease and DI characteristics varied across pathways. Pathway types varied regionally. A higher proportion in the 'primary care only' pathway lived in rural areas whereas a higher proportion in the 'referred to specialist for immediate action' and the 'specialist care only' pathways lived in major urban centres. Across pathways, the median DI varied from 1 to 67 days, the median primary care subinterval varied from 1 to 30 days and the median specialist care subinterval varied from 1 to 25 days. Patients in the 'primary care only' pathway experienced the shortest DIs, and patients in the 'multiple visits and procedures in specialist care' pathway experienced the longest DIs. CONCLUSIONS AND RELEVANCE: We identified four melanoma diagnostic pathways. The shortest DI, the 'primary care only' pathway, highlights the important role of primary care and the need to reduce the wait for specialists. Diagnostic processes varied across geographical locations. Future research should address reasons for these differences, including whether they are associated with inefficient or inappropriate 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".