Prevalence of autoimmune inflammatory myopathy in the first nations population of Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: To estimate the population-based prevalence of autoimmune inflammatory myopathy (AIM) in Alberta, Canada, with a specific focus on rates in the First Nations population. METHODS: Physician billing claims and hospitalization data for the province of Alberta (1994-2007) were used to estimate the probability of having AIM (i.e., polymyositis or dermatomyositis) based on 3 case definitions. A latent class Bayesian hierarchical regression model was employed to account for the imperfect sensitivity and specificity of billing and hospitalization data in case ascertainment. We accounted for demographic factors of sex, age group, and location of residence (urban or rural) in estimating the prevalence rates within the First Nations and non-First Nations populations. RESULTS: The overall prevalence of AIM was 25.0 per 100,000 persons (95% credible interval [95% CrI] 13.4-49.0) in the First Nations population and 33.8 (95% CrI 28.9-39.6) in the non-First Nations population. For both groups, prevalence was increased in women relative to men, rural women relative to urban women, and in those age >45 years. CONCLUSION: Unlike other rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, and systemic sclerosis, we did not detect an increased prevalence of AIM in Alberta's First Nations population relative to the non-First Nations population. Potential limitations include coding errors, underidentification of First Nations members, and recognized differences in access to care for the First Nations 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".