Incidence and Mortality Trends and Geographic Patterns of Follicular Lymphoma in Canada
Bibliographic record
Abstract
Background: Follicular lymphoma (fl) is the most common indolent lymphoma and the 2nd most common non-Hodgkin lymphoma, accounting for 10%-20% of all lymphomas in the Western world. Epidemiologic and geographic trends of fl in Canada have not been investigated. Our study's objective was to analyze incidence and mortality rates and the geographic distribution of fl patients in Canada for 1992-2010. Methods: Demographic and geographic patient data for fl cases were obtained using the Canadian Cancer Registry, the Registre québécois du cancer, and the Canadian Vital Statistics database. Incidence and mortality rates and 95% confidence intervals were calculated per year and per geographic area. Rates were plotted using linear regression models to assess trends over time. Overall data were mapped using Microsoft Excel mapping software (Redmond, WA, U.S.A.) to identify case clusters across Canada. Results: Approximately 22,625 patients were diagnosed with fl during 1992-2010. The age-standardized incidence rate of this malignancy in Canada was 38.3 cases per million individuals per year. Geographic analysis demonstrated that a number of Maritime provinces and Manitoba had the highest incidence rates, and that the provinces of Nova Scotia and Quebec had the highest mortality rates in the nation. Regional data demonstrated clustering of fl within cities or regions with high herbicide use, primary mining, and a strong manufacturing presence. Conclusions: Our study provides a comprehensive overview of the fl burden and its geographic distribution in Canada. Regional clustering of this disease in concentrated industrial zones strongly suggests that multiple environmental factors might play a crucial role in the development of this lymphoma.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.002 | 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".