Incidence of differentiated thyroid cancer in Canada by City of residence
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer incidence in Canada is increased in high socioeconomic groups, and in urban compared with rural areas. The objective of this study was to analyze patterns in thyroid cancer incidence across Canada, particularly with respect to the major urban areas across the country, to identify whether there are any discrepancies in thyroid cancer incidence between Canadian cities. METHODS: Cases were drawn from the Canadian Cancer Registry. Demographic and socioeconomic information were extracted from the Canadian Census of Population data. We linked cases to income quintiles (InQs) by patients' postal codes, and categorized residence by census metropolitan area ((CMA), population >100,000). Within the Toronto CMA we further classified by census subdivision (CSD). RESULTS: There were a total of 33 CMAs across the country. After controlling for demographic and socio-economic factors, we found that the Toronto CMA had an IRR of thyroid cancer that was significantly higher than all other CMAs across the country. For 70% of CMAs and CAs across Canada, the IRR for thyroid cancer was less than half of the IRR for thyroid cancer in the Toronto CMA. As Toronto is one of the largest CMAs, we then subdivided the Toronto area into CSDs to examine how incidence of thyroid cancer varies within this large area. The Toronto City core was used as the reference category and all other areas were compared directly to it. In doing so, we found that a contiguous area of three CSDs North of Toronto had higher IRRs compared with the Toronto city core: Markham, Vaughan and Richmond Hill. CONCLUSIONS: After controlling for demographic and socioeconomic factors, we found that the Toronto CMA has the highest incidence of thyroid cancer nationwide. Several explanations could account for this discrepancy including increased detection due to increased access to imaging, differences in ethnicity or environmental exposures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".