Scleroderma prevalence: Demographic variations in a population‐based sample
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of systemic sclerosis (SSc) using population-based administrative data, and to assess the sensitivity of case ascertainment approaches. METHODS: We ascertained SSc cases from Quebec physician billing and hospitalization databases (covering approximately 7.5 million individuals). Three case definition algorithms were compared, and statistical methods accounting for imperfect case ascertainment were used to estimate SSc prevalence and case ascertainment sensitivity. A hierarchical Bayesian latent class regression model that accounted for possible between-test dependence conditional on disease status estimated the effect of patient characteristics on SSc prevalence and the sensitivity of the 3 ascertainment algorithms. RESULTS: Accounting for error inherent in both the billing and the hospitalization data, we estimated SSc prevalence in 2003 at 74.4 cases per 100,000 women (95% credible interval [95% CrI] 69.3-79.7) and 13.3 cases per 100,000 men (95% CrI 11.1-16.1). Prevalence was higher for older individuals, particularly in urban women (161.2 cases per 100,000, 95% CrI 148.6-175.0). Prevalence was lowest in young men (in rural areas, as low as 2.8 cases per 100,000, 95% CrI 1.4-4.8). In general, no single algorithm was very sensitive, with point estimates for sensitivity ranging from 20-73%. CONCLUSION: We found marked differences in SSc prevalence according to age, sex, and region. In general, no single case ascertainment approach was very sensitive for SSc. Therefore, using data from multiple sources, with adjustment for the imperfect nature of each, is an important strategy in population-based studies of SSc and similar conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".