Incidence of Multiple Sclerosis and Related Disorders in Asian Populations of British Columbia
Bibliographic record
Abstract
BACKGROUND: Global variation in the incidence of multiple sclerosis (MS) is generally ascribed to differences in genetic and environmental risk factors. Here we investigate temporal trends in the incidence of MS and related disorders in British Columbia, Canada, from 1986 to 2010, focusing particularly on the Asian ethnic subpopulation. METHODS: A longitudinal database was screened to identify newly diagnosed cases of MS and related disorders, including neuromyelitis optica and clinically isolated syndromes. Age-standardized, sex-specific mean annual incidence was calculated for the Asian and non-Asian population of British Columbia for 5-year intervals from 1986 to 2010. Temporal changes and cohort differences in incidence rates and demographic characteristics were evaluated. RESULTS: During this period, the incidence of MS and related disorders in the non-Asian population remained relatively unchanged, from 10.41 (95% confidence interval [CI]: 9.87-10.97) to 9.91 (95% CI: 9.46-10.39) per 100,000 (p=0.167). In contrast, incidence in the Asian population doubled during the same period. This increase was driven by a precipitous rise in the incidence of MS in females from 0.71 (95% CI: 0.01-1.50) to 2.08 (95% CI: 1.43-2.91) per 100,000 (p=0.004), including both Canadian-born and immigrant Asians. The incidence of neuromyelitis optica did not change significantly during this period. CONCLUSIONS: The incidence of MS may be increasing among females in the Asian ethnic population of British Columbia.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".