Epidemiology and Survival of Systemic Sclerosis-Systemic Lupus Erythematosus Overlap Syndrome
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) may overlap with systemic lupus erythematous (SLE). Little is known about the epidemiology, clinical characteristics, and survival of SSc-SLE overlap. We evaluated the prevalence of SSc-SLE overlap and differences in SSc characteristics, and compared survival with SSc without SLE. METHODS: A cohort study was conducted including subjects who fulfilled the American College of Rheumatology (ACR)/European League Against Rheumatism classification criteria for SSc and/or the ACR criteria for SLE. The primary outcome was time from diagnosis to all-cause mortality. Survival was evaluated using Kaplan-Meier and Cox proportional hazard models. RESULTS: We identified 1252 subjects (SSc: n = 1166, SSc-SLE: n = 86) with an SSc-SLE prevalence of 6.8%. Those with SSc-SLE were younger at diagnosis (37.9 yrs vs 47.9 yrs, p < 0.001), more frequently East Asian (5.5% vs 20%) or South Asian (5.1% vs 12%), had lupus anticoagulant (6% vs 0.3%, p < 0.001), anticardiolipin antibody (6% vs 0.9%, p < 0.001), and pulmonary arterial hypertension (PAH; 52% vs 31%, p < 0.001). Those with SSc-SLE less frequently had calcinosis (13% vs 27%, p = 0.007), telangiectasia (49% vs 75%, p < 0.001), and diffuse subtype (12% vs 35%, p < 0.001). There were no significant differences in the occurrence of renal crisis (7% vs 7%), interstitial lung disease (ILD; 41% vs 34%), and digital ulcers (38% vs 32%). Those with SSc-SLE had better median survival time (26.1 vs 22.4 yrs), but this was not statistically significant (log-rank p = 0.06). Female sex and diffuse subtype attenuated survival differences between groups (HR 1.07, 95% CI 0.67-1.67). CONCLUSION: Patients with SSc-SLE are younger at diagnosis, more frequently have PAH, and less frequently have cutaneous manifestations of SSc. They should be monitored for ILD, renal crisis, and digital ulcers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".