Geographic distribution of Multiple Sclerosis (MS) mortality rates in Canada, 1975-2009
Bibliographic record
Abstract
Background: Our study examined whether there are differences in MS mortality rates across regions of Canada, which might suggest differences in environment or health care practice that influence outcome. Methods: Statistics Canada data on deaths due to MS and populations at risk, 1975-2009, were derived from the Research Data Centre, University of Alberta. Mortality rates and 95% confidence intervals (CIs) were calculated per 100,000 population for the Atlantic Provinces, Quebec, Ontario and Western Provinces (including Northwest Territories, Yukon, Nunavut), age-standardized to the 2006 population. Results: The average annual MS mortality rates for 1975-2009 per 100,000 population (CIs) were: Atlantic Provinces 1.09 (0.43,1.74); Quebec 1.30 (0.89,1.71); Ontario 1.08 (0.77,1.38); Western Provinces 1.39 (0.99,1.78). Female mortality rates were consistently higher than male rates but there were no differences in the female:male mortality rate ratios across regions. Trend analysis showed that rates were stable over the 35 year time span in 3 regions with non-significant average annual per cent increases/decreases of: Atlantic Provinces –0.43%; Quebec +0.12%; and Western Provinces +0.27%. Only Ontario showed a slight but significant increase of +0.81% (p<0.05). Conclusions: MS mortality rates are similar across the Canadian regions, suggesting that patients are not disadvantaged in terms of mortality by their place of residence.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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".