Systemic Sclerosis and Associated Interstitial Lung Disease in Ontario, Canada: An Examination of Prevalence and Survival Over 10 Years
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a rare autoimmune disease. Pulmonary complications of SSc are some of the leading causes of morbidity and mortality. The objective of this study was to determine prevalence and survival estimates of SSc and SSc with interstitial lung disease (SSc-ILD) in the Canadian province of Ontario using administrative data over 10 years. METHODS: Using International Classification of Diseases, 10th revision codes adapted for Canada (ICD-10-CA), adult patients diagnosed with SSc and SSc-ILD between April 1, 2008, and March 31, 2018, were identified from the National Ambulatory Care Reporting System and the Discharge Abstract Database administrative databases. SSc was identified first, and ILD was included if presence occurred after SSc diagnosis. Prevalence estimates were determined for both SSc and SSc-ILD. For survival rates, Kaplan-Meier survival curves were generated. RESULTS: At the start of the 2017/18 fiscal year (final year of the cohort), there were 2114 prevalent SSc cases for a cumulative prevalence of 19.1 per 100,000 persons, as well as 257 prevalent cases of SSc-ILD that generated a prevalence of 2.3 cases per 100,000 persons. Mean ages were 57 and 58 years with 84% and 80% females for patients with SSc and SSc-ILD, respectively. One-, 5-, and 10-year survival rates were 85.0%, 64.5%, and 44.9% for the SSc group and 77.1%, 44.4%, and 22.0% for the SSc-ILD group, respectively. CONCLUSION: To our knowledge, this study provides the first population-based estimates of SSc and SSc-ILD in Canada for prevalence and survival. Results confirm that the prevalence estimates of SSc-ILD fall within the Canadian threshold for rare disease. It also demonstrates the poor survival in SSc, especially when ILD is also present.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".