Multiple sclerosis in the Iranian immigrant population of BC, Canada: prevalence and risk factors
Bibliographic record
Abstract
BACKGROUND: There is a well-documented increase in the risk of multiple sclerosis (MS) when migrating from a region of low prevalence to one of high prevalence. OBJECTIVE: We present here an investigation of MS prevalence and candidate environmental and genetic risk factors among Iranian immigrants to British Columbia (BC), Canada. METHODS: MS cases of Iranian ancestry were ascertained from a population-based Canadian study. We collected blood samples for genetic and serological analyses, and administered a personal history questionnaire to the cases. RESULTS: The crude prevalence of MS in this population of Iranian ancestry was 287/100,000 (95% CI: 229 - 356/100,000). MS cases were more likely to have a history of infectious mononucleosis (odds ratio (OR) = 7.5; p = 0.005) and smoking (OR = 17.0; p < 0.0001), as compared to healthy controls from previous studies in Iran. Cases were also more likely than controls to have been born between April and September (OR = 2.1; p = 0.019). CONCLUSION: The prevalence of MS among Iranian immigrants to Canada is greater than the overall prevalence of MS in Iran by a factor of at least four, and is similar to that recently observed among Iranian immigrants in other western nations. No major genetic susceptibility variants were identified, suggesting the environment in Canada may be what is increasing the risk of MS in this population.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".