Incidence and prevalence of multiple sclerosis in Saskatoon, Saskatchewan
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of multiple sclerosis (MS) in a longitudinal surveillance over 35 years and to estimate the prevalence rate in Saskatoon, Saskatchewan, on January 1, 2005. METHODS: A population-based registry was established in 1969, and identification of cases continued to 2005, from medical records, physicians, neurologists, community and provincial resources. A modified classification of Allison and Millar and the Schumacher diagnostic criteria were originally applied, and patients with definite and probable MS were included. The rates were age- and sex-adjusted to the US, European, and world 2000 populations. RESULTS: From 1970 to 2004, there were 558 incidence cases identified, 402 women and 156 men, for a sex ratio of 2.6:1 The average annual incidence rate was 9.5 in 100,000 (95% CI 8.8 to 10.4) and was stable over the three decades. The innate risk or residence at onset rate was 197 in 100,000 (95% CI 170 to 226). The crude prevalence rate for the living 587 cases on January 1, 2005, was 298.3 in 100,000 (95% CI 274.7 to 323.6). CONCLUSIONS: The incidence and prevalence rates adjusted to the standardized populations were statistically higher than the longitudinal European studies and similar to North American studies. Our incidence study confirms the high risk of multiple sclerosis (MS) in Saskatoon, and these rates seem to be stable over the past 35 years. The high crude prevalence rate results from an accumulation of incidence and nonresident cases over time. Long-term follow-up studies and comparison with standardized populations are recommended to estimate reliable incidence and the true risk of MS in the world.
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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 | 0.003 |
| Science and technology studies | 0.000 | 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".