Analysis of acute myeloid leukemia incidence and geographic distribution in Canada from 1992 to 2010 reveals disease clusters in Sarnia and other industrial US border cities in Ontario
Bibliographic record
Abstract
BACKGROUND: Several risk factors have been implicated in acute myeloid leukemia (AML) leukemogenesis. However, the epidemiologic distribution and precise triggers for AML in Canada remain poorly understood. METHODS: In this study, demographic data for AML patients in Canada from 1992 to 2010 were analyzed using 3 independent population-based cancer registries. The AML incidence and mortality rates were examined at the levels of province/territory, city, and forward sortation area (FSA) postal code. RESULTS: In total, 18,085 patients were identified. AML incidence was documented to be 30.61 cases per million individuals per year (95% confidence interval [CI], 30.17-31.06) from 1992 to 2010. Five industrial cities in Ontario were identified where incidence rates were significantly higher than the national average: Sarnia, Sault Ste. Marie, Thunder Bay, St. Catharines, and Hamilton. Analysis at the FSA postal code level identified significant patient clusters of AML in these cities. Specifically, FSA N7V in Sarnia, Ontario had an incidence of 106.81 (95% CI, 70.96-161.86) cases per million individuals per year, which is >3 times higher than the national average. The pollution from local oil refineries and chemical plants in Sarnia may be implicated as a risk factor for AML in that city. Analysis of mortality rates at the province and city levels corroborated the findings from the incidence data. CONCLUSION: These results provide a comprehensive analysis of AML burden in Canada and reveal striking geographic case clustering in industrial Ontario cities and potentially implicate exposure to materials/pollution from these plants as an important risk factor for developing AML in Canada.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".