Long-Term Epidemiology of Systemic Sclerosis in Western Australia: A Population-Level Linked Data Study
Bibliographic record
Abstract
Objective To report the prevalence, incidence and mortality of systemic sclerosis (SSc) in Western Australia (WA). Methods This was a retrospective observational study, using whole-population linked administrative health data from the WA Rheumatic Disease Epidemiological Registry. All patients with an incident (first-ever) hospitalization with SSc between 1985 and 2013 were identified from discharge diagnosis fields and followed until end of 2014. Outcome measures were incidence rate (IR), point prevalence, standardized mortality ratio (SMR), and survival estimates using Cox regression, stratified by sex. Results In total 877 patients (mean age 58.6 years, 77.8% female, 3.2% Aboriginal and/or Torres Straits Islander people) had an incident hospitalization for SSc. The age-standardized IR of SSc ranged from 0.44 to 3.26 per 100,000 person-years and point prevalence averaged 37.93 per 100,000 population; both were higher for female individuals. During the study period, 452 (51.5%) patients died with crude mortality higher in male than female patients (66.2% vs 47.4%; P < 0.001). The SMR was 4.17 (95% CI 3.81-4.58), whereas 5- and 10-year survival rates were 67% and 52.4%, respectively. Age (hazard ratio [HR] 1.05, 95% CI 1.04-1.05), male sex (HR 1.56, 95% CI 1.27-1.92), heart failure (HR 1.88, 95% CI 1.36-2.60), kidney disease (HR 1.71, 95% CI 1.13-2.58), and cancer (HR 1.88, 95% CI 1.30-2.74) were independently associated with death. The main causes of death were SSc (n = 128, 28.3%), solid organ malignancy (n = 65, 14.4%), and ischemic heart disease (n = 47; 10.4%). Conclusion The burden of SSc in WA exceeds global estimates and its high prevalence, high SMR, and number of deaths due to SSc as a primary cause suggest a large unmet therapeutic need.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".