Bibliographic record
Abstract
Scleroderma is a connective tissue disease that causes fibrosis and vascular abnormalities, but that also has an autoimmune component. Many patients with scleroderma have a positive antinuclear antibody, and there can be family histories of other connective tissue diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis. Some patients have features of scleroderma and other autoimmune conditions. This article will review recent literature to help in the understanding of scleroderma with overlap features. Recent reports of scleroderma overlap with rheumatoid arthritis suggest distinct features of diffuse scleroderma with positive Scl-70, pulmonary fibrosis, and later seropositive erosive rheumatoid arthritis. SLE rarely occurs with scleroderma. Sjögren syndrome symptoms are common in scleroderma. In primary Sjögren syndrome, anticentromere antibody positive patients have more Raynaud phenomenon. Antibodies that occur in scleroderma that are thought to be specific are present in other connective tissue diseases. For instance, Scl-70 antibody is reported in as many as 35% of patients with scleroderma but can be present in 25% of patients with SLE. Myositis or myopathy can be features of scleroderma. Scleroderma overlap with polymyositis is frequently anti-PM Scl antibody positive, whereas anti-Jo-1 does not normally occur in the overlap of scleroderma and polymyositis but is usually exclusively positive in polymyositis with arthritis and alveolitis. A better prognosis is found with PM Scl antibody in myositis. Vasculitis is not a typical feature of scleroderma, but has been reported. Eosinophilic fasciitis is rare, and the onset could be associated with simvastatin.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".