ORIGINAL COMMUNICATION High incidence and increasing prevalence of multiple sclerosis in British Columbia, Canada: findings from over two decades (1991–2010)
Bibliographic record
Abstract
The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Province-wide population-based administrative health data from British Columbia (BC), Canada (popula-tion: approximately 4.5 million) were used to estimate the incidence and prevalence of multiple sclerosis (MS) and examine potential trends over time. All BC residents meeting validated health administrative case definitions for MS were identified using hospital, physician, death, and health registration files. Estimates of annual prevalence (1991–2008), and incidence (1996–2008; allowing a 5-year disease-free run-in period) were age and sex standardized to the 2001 Canadian population. Changes over time in incidence, prevalence and sex ratios were examined using Poisson and log-binomial regression. The incidence rate was stable [average: 7.8/100,000 (95 % CI 7.6, 8.1)], while the female: male ratio decreased (p = 0.045) but remained at or above 2 for all years (average 2.8:1). From 1991–2008, MS prevalence increased by 4.7 % on average per year (p \\ 0.001) from 78.8/100,000 (95 % CI 75.7, 82.0) to 179.9/100,000 (95 % CI 176.0, 183.8), the sex prevalence ratio increased from 2.27 to 2.78 (p \\ 0.001) and the peak prevalence age range increased from 45–49 to 55–59 years. MS incidence and prevalence in BC are among the highest in the world. Neither the incidence nor the incidence sex ratio increased over time. However, the prevalence and prevalence sex ratio increased significantly during the 18-year period, which may be explained by the increased peak prevalence age of MS, longer survival with MS and the greater life expectancy of women compared to men.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".