What have multicentre registries across the world taught us about the disease features of systemic sclerosis?
Bibliographic record
Abstract
Introduction The aim of this study is to compare the clinical features, mortality and causes of death of systemic sclerosis (SSc) patients in four large multicentre registries. Methods Patients seen at least once in the Australian Scleroderma Cohort Study (ASCS) (n = 1714), the Canadian Scleroderma Research Group (CSRG) (n = 1628), the European League Against Rheumatism Scleroderma Trials and Research (EUSTAR) Network (n = 13,996) and the Systemic Sclerosis Cohort in Singapore (SCORE) (n = 500) before August 2016 were included. Clinical manifestations and survival in cohorts and disease subtypes were compared. Results Among 17,838 SSc patients, most were female (86.1%), Caucasian (84.6%) and had the limited cutaneous subtype (lcSSc) (65.0%). The anti-centromere autoantibody was the most prevalent (37.6%). More patients in SCORE had the diffuse subtype (dcSSc) (49.3%) and Scl-70 autoantibody (38.8%) (p<0.001). Patients with dcSSc were more likely to be younger and male (p<0.001) and have shorter disease duration, more calcinosis, tendon friction rubs and synovitis (all p<0.001). Interstitial lung disease (ILD) occurred more frequently in dcSSc but prevalence of pulmonary arterial hypertension (PAH) was similar in both subtypes. More deaths occurred among SCORE patients who had the shortest median survival (p<0.001). The survival of patients with early disease, males and those with dcSSc was shorter than that of patients with prevalent disease, female gender and lcSSc, respectively. SSc-related complications accounted for more than 50% of deaths, with PAH and ILD being the most common. Conclusions This meta-cohort of SSc patients, the largest reported to date, provides insights into the impact of race and sex on disease manifestations and survival and confirms the early mortality in this disease.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".