Myopathy is a poor prognostic feature in systemic sclerosis: results from the Canadian Scleroderma Research Group (CSRG) cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the clinical impact of muscle involvement in a large systemic sclerosis (SSc) cohort. METHOD: Using the Canadian Scleroderma Research Group (CSRG) database, SSc patients with either elevated creatine kinase (CK) or a prior history of myositis/myopathy were identified. Regression and Kaplan-Meier analyses were performed to determine characteristics associated with muscle involvement in SSc and survival outcome. RESULTS: In 1145 patients with SSc, 5.6% had an elevated CK. This subset was more likely to be male (24.5% in elevated CK vs. 12.6% in normal CK, p < 0.013), younger (52 vs. 56 years, p < 0.045), have diffuse cutaneous SSc (dcSSc; 40.4% vs. 37.9%, p < 0.002), tendon friction rubs (30.0% vs. 13.4%, p < 0.001), and forced vital capacity (FVC) < 70% (23.9% vs. 13.1%, p < 0.039), be ribonucleoprotein (RNP) antibody positive (12.0% vs. 5.0%, p < 0.032), topoisomerase1 (topo1)-antibody positive (26.0% vs. 14.4%, p < 0.026), have a higher modified Rodnan skin score (MRSS; 16.14 vs. 9.81, p < 0.001), and a higher Health Assessment Questionnaire (HAQ) score (0.98 vs. 0.79, p < 0.011). Survival was reduced for patients with elevated CK (p < 0.025). Nearly 10% of patients in the CSRG cohort had a prior history of myositis/myopathy. This subset also had findings similar to those with elevated CK and increased mortality (p < 0.003). CONCLUSIONS: Muscle involvement in SSc has a poor prognosis impacting survival, especially in men with early dcSSc with topo1 and RNP autoantibodies and interstitial lung disease (ILD).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".