Epidemiology of systemic sclerosis in Quebec, Canada: a population-based study
Bibliographic record
Abstract
Background: Systemic sclerosis (SSc) is a systemic life-threatening autoimmune rheumatic disease. We aimed to assess the incidence, prevalence, mortality and spatiotemporal trends of SSc in Quebec, Canada with stratification by sex and age. Methods: SSc cases were identified from Quebec populational databases from 1989 to 2019. Negative Binomial (NB) Generalized Linear Models were used for age-standardized incidence rates (ASIR) analyses and NB random walk for prevalence and mortality. A Poisson Besag-York-Mollié regression model was used for spatial analysis. Findings: 8180 incident SSc cases were identified between 1996 and 2019 with an average age of 57.3 ± 16.3 years. The overall ASIR was 4.14/100,000 person-years (95%, Confidence Interval (CI) 4.05-4.24) with a 4:1 female predominance. ASIR increased steadily over time with an Average Annual Percent Change (AAPC) of 3.94% (95% CI 3.49-4.38). While the highest incidence rates were in those aged 60-79 years old among females and >80 years old among males, the highest AAPC (∼10%) was seen in children. Standarized incidence ratios varied geographically between 0.52 to 1.64. The average prevalence was 28.96/100,000 persons (95% CI 28.72-29.20). The Standardized Mortality Ratio (SMR) decreased from 4.18 (95% CI 3.64-4.76) in 1996 to 2.69 (95% CI 2.42-2.98) in 2019. Females had a greater SMR until 2007 and males thereafter. The highest SMR was in children and young adults [31.2 (95% CI 8.39-79.82) in the 0-19-year age group]. Interpretation: We showed an increasing trend in SSc incidence and prevalence and a decline in SMR over a 25-year period in Quebec. An uneven geographic distribution of SSc incidence was demonstrated. Funding: National Scleroderma Foundation, Canadian Dermatology Foundation/Canadian Institutes of Health Research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".