Systemic Sclerosis Sine Scleroderma: A Time of Reappraisal
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis sine scleroderma (ssSSc), formally described in 1962, is a subset of SSc that, unlike limited cutaneous (lcSSc) and diffuse cutaneous (dcSSc) forms, lacks skin fibrosis. According to the 2013 American College of Rheumatology/European Alliance of Associations for Rheumatology criteria, SSc can be diagnosed in the absence of skin thickening, even if this is expected to develop later in disease course. Driven by a fatal case of ssSSc with cardiac involvement, we analyzed published data on ssSSc prevalence and severity. METHODS: A systematic literature review and qualitative synthesis of SSc cohorts with data on ssSSc were performed. RESULTS: Thirty-five studies involving a total of 25,455 patients with SSc, published between 1976 and 2023, were identified. Although different definitions were used, the mean prevalence of ssSSc was almost 10% (range 0-23%), with the largest study reporting a cross-sectional prevalence of 13%. In 5 studies with a follow-up period of up to 9 years, reclassification of ssSSc into lcSSc or dcSSc ranged 0-28%. Interstitial lung disease, pulmonary arterial hypertension, scleroderma renal crisis, and cardiac diastolic dysfunction were present in 46% (range 9.3-59.1%), 15% (range 5.9-24.6%), 5% (range 1.6-24.6%), and 26.5% (range 1.8-40.7), respectively, of patients with ssSSc. Survival across studies was comparable to lcSSc and better than dcSSc. CONCLUSION: Published data on ssSSc vary widely on prevalence, clinical expression, and prognosis, partly due to underdiagnosis and misclassification. Although classification criteria should not affect appropriate management of patients, updated ssSSc subclassification criteria that takes into account time from disease onset should be considered.
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 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 | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".