Identification of significant geographic clustering of polycythemia vera cases in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated at times with debilitating symptoms and a significant mortality rate. Understanding the demographics, epidemiology, and geography of this disease may provide further insight into important risk factors associated with its development. The objective of this study was to analyze patient demographics, incidence, and mortality rates, as well as the geographic distribution of PV patients in Canada between 1992 and 2010. METHODS: This study was achieved by analyzing the Canadian Cancer Registry, Le Registre Québécois du Cancer, and the Canadian Vital Statistics patient databases. RESULTS: A total of 4645 patients were diagnosed with PV between 1992 and 2010. While the annual incidence rate of this cancer fluctuated in Canada, mortality rate analysis indicated a decreasing trend. Geographically, PV incidence rates were notably elevated in the province of Quebec compared with the Canadian average. Further analysis of high-incidence forward sortation areas indicated a striking clustering of cases in the H4W region encompassing the Côte-Saint-Luc borough of Montreal, with an incidence of 102.97 (95% confidence interval, 75.11-137.79) cases per million per year, which is >13 times the national average. CONCLUSION: The residential area of Côte-Saint-Luc is an important PV cluster in Canada, with high concentration of retirement homes and geriatric hospices. Also, Jewish residents comprise >60% of the population in this neighborhood. These findings suggest that an older age and, potentially, an inherent genetic predisposition may be implicated in the pathogenesis of this malignancy. This study provides a comprehensive overview of PV burden/geographic distribution of cases 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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".