Epidemiology and Survival of Systemic Sclerosis–Sarcoidosis Overlap Syndrome
Bibliographic record
Abstract
Objective We evaluated the epidemiology, manifestations, serology, comorbidities, and survival among patients with systemic sclerosis (SSc) with and without sarcoidosis. Methods We conducted a retrospective cohort study comparing patients with SSc with and without sarcoidosis. All patients fulfilled the American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for SSc. Sarcoidosis was based on physician diagnosis and/or confirmatory biopsy. The primary outcome was time from diagnosis to all-cause mortality. Survival was evaluated using Kaplan-Meier curves. Results We included 1977 patients (1971 with SSc, 6 with SSc–sarcoidosis) with a SSc–sarcoidosis prevalence of 0.30%. Sarcoidosis frequently preceded SSc (66.66%). The most frequent sarcoidosis manifestations were pulmonary (66.66%), lymphadenopathy (66.66%), arthritis (50%), cutaneous (33.33%), and hepatic (16.66%). Patients with SSc and SSc–sarcoidosis had female to male sex ratios of 4.5:1 vs 5:1 and median ages of SSc onset of 48.3 vs 43.8 years, respectively. Interstitial lung disease (35% vs 66.66%) and pulmonary hypertension (24.91% vs 50%) tended to occur more frequently whereas abnormal nailfold capillaries (34.7% vs 16.66%) and digital ulcers (33.33% vs 16.66%) tended to occur less frequently among patients with SSc–sarcoidosis, but the differences were not significant. There was an increased frequency of stroke among the patients with SSc–sarcoidosis (relative risk 8.59, 95% CI 1.02-72.00). The median survival times were 23.4 years for SSc–sarcoidosis and 18.6 years for SSc, with no differences in survival curves (log-rank test,P =0.55). Conclusion Sarcoidosis in SSc is rare but appears to occur more frequently than in the general population. It is associated with pulmonary, lymph node, cutaneous, joint, and hepatic involvement. Stroke occurs more frequently in patients with SSc–sarcoidosis but with no differences in survival.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".