Systemic Sclerosis Sine Scleroderma: A Multicenter Study of 1417 Subjects
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical and serological features of systemic sclerosis sine scleroderma (ssSSc) in a multicentered SSc cohort. METHODS: Data from 1417 subjects in the Canadian Scleroderma Research Group registry were extracted to identify subjects with ssSSc, defined as SSc diagnosed by an expert rheumatologist, but without any sclerodactyly or skin involvement prior to baseline study visit or during followup. Clinical and serological features of ssSSc subjects were compared to limited (lcSSc) and diffuse cutaneous SSc (dcSSc) subjects. RESULTS: At the first registry visit, only 57 subjects (4.0%) were identified as having ssSSc. Of these, 30 (2.1%) were reclassified as lcSSc within 1.9 years. Thus, only 27 ssSSc subjects (1.9%) remained, with mean followup of 2.4 years. Clinical profiles of ssSSc were generally similar or milder compared to lcSSc, and milder than dcSSc, including rates of interstitial lung disease (25.9% ssSSc, 25.4% lcSSc, 40.3% dcSSc). Patients with ssSSc had serological profiles similar to those with lcSSc, including high rates of anticentromere antibodies (50.0% ssSSc, 47.5% lcSSc, 12.1% dcSSc), and low rates of antitopoisomerase I (16.7% ssSSc, 7.0% lcSSc, 21.8% dcSSc) and anti-RNA polymerase III (0 ssSSc, 11.1% lcSSc, 34.9% dcSSc). CONCLUSION: The condition ssSSc is rare and resembles lcSSc. These observations suggest that ssSSc is most likely a forme fruste of lcSSc, and that the absence of skin involvement may in part be related to misclassification arising from early or subtle skin involvement. There is little evidence to consider ssSSc as a distinct clinical or serological subset of SSc.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".