Clinical and Serological Hallmarks of Systemic Sclerosis Overlap Syndromes
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of systemic sclerosis (SSc) overlap syndrome and autoantibody profile in a large single-center cohort. METHODS: SSc diagnoses, subsets, and autoantibody profiles were obtained from clinical records of patients attending the Centre for Rheumatology, Royal Free Hospital, between September 1999 and February 2007. RESULTS: In total, 332 (20%) of 1700 patients with SSc had overlap syndrome. This comprised myositis (42.8%), rheumatoid arthritis (RA; 32%), Sjögren's syndrome (SS; 16.8%), and systemic lupus erythematosus (SLE; 8.4%). Antinuclear antibody was positive in 96.6% of patients. Anticentromere antibody (ACA) was exclusively present in limited cutaneous SSc (lcSSc) overlap cases (22%), and more common in SSc/SS overlap (44.7%), whereas no difference was found in the prevalence of Scl-70 autoantibody between lcSSc and diffuse cutaneous SSc overlap groups. U1RNP was more frequent in SSc/SLE (44%), while Ro antibody was more likely to be found in SSc/SS (29.8%). ACA was absent and anti-Scl-70 was infrequent in SSc/myositis; polymyositis-scleroderma antibody was more frequent in this group (33.1%). About 50% of patients had raised rheumatoid factor (RF), with no difference between overlap groups irrespective of RF titer. In contrast, anticyclic citrullinated peptide antibody was more frequent in patients with RA features. CONCLUSION: About one-fifth of SSc cases had overlap features. There were distinct serological features that may predict specific clinical presentation and disease course.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".