Multiple myeloma epidemiology and patient geographic distribution in Canada: A population study
Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM) is a malignancy of mature plasma cells. Environmental risk factors identified for this malignancy, among others, include farming and exposure to pesticides. METHODS: Using 3 independent population-based databases (the Canadian Cancer Registry, le Registre Québécois du Cancer, and Canadian Vital Statistics), this study analyzed patients' clinical characteristics and the incidence, mortality, and geographic distribution of MM cases in Canada during 1992-2015. RESULTS: In total, ~32,065 patients were identified, and 53.7% were male. The mean age at the time of diagnosis was 70 ± 12.1 years. The average incidence rate in Canada was 54.29 cases per million individuals per year, and linear regression modeling showed a steady rise in the annual rate of 0.96 cases per million individuals per year. At the provincial level, Quebec and Ontario had significantly higher incidence rates than the rest of Canada. An analysis of individual municipalities and postal codes showed lower incidence rates in large metropolitan areas and in high-latitude regions of the country, whereas high incidence rates were observed in smaller municipalities and rural areas. Land use analysis demonstrated increased density of crop farms and agricultural industries in high-incidence areas. A comparison with the available data from 2011-2015 showed several consistent trends at provincial, municipal, and regional levels. CONCLUSIONS: These results provide a comprehensive analysis of the MM burden in Canada. Large metropolitan cities as well as high-latitude regions were associated with lower MM incidence. Higher incidence rates were noted in smaller cities and rural areas and were associated with increased density of agricultural facilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".