Prevalence of Multiple Sclerosis in First Nations People of Alberta
Bibliographic record
Abstract
BACKGROUND: Multiple Sclerosis (MS) is reported to be uncommon among North American aboriginals despite frequent intermarriage with people of European ancestry, but few population-based studies have been conducted. The purpose of this study was to determine the prevalence of MS among First Nations aboriginal people in Alberta, Canada compared to the general population. METHODS: All hospital in-patient and physician fee-for-service records between 1994 and 2002 where a diagnosis of MS was mentioned were extracted from government health databases in the province of Alberta. First Nations people can be identified since the federal government (Health Canada) pays health care insurance premiums on their behalf. Multiple Sclerosis prevalence per 100,000 population for both First Nations people and the general population of Alberta were calculated for each year during this time span. RESULTS: Among First Nations in Alberta, MS prevalence was 56.3 per 100,000 in 1994 and 99.9 per 100,000 in 2002, an increase of 43.6%. In 2002 prevalence was 158.1 and 38.0 for females and males respectively, a female to male ratio of 4.2:1. Multiple Sclerosis prevalence among the general population of Alberta was 262.6 per 100,000 in 1994 and 335.0 per 100,000 in 2002, an increase of 21.6%. In 2002 prevalence was 481.5 and 187.5 for females and males respectively, a female to male ratio of 2.6:1. Peak prevalence for both First Nations and general population females in 2002 was age 50-59, also 50-59 for both First Nations and general population males. CONCLUSION: While MS prevalence in First Nations people is lower than in the general population of Alberta, it is not rare by worldwide standards.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".