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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".