Autoantibody profiles delineate distinct subsets of scleromyositis
Bibliographic record
Abstract
OBJECTIVE: Scleromyositis remains incompletely characterized owing in part to its heterogeneity. The purpose of this study was to explore the role of autoantibody profiles to define subsets of scleromyositis. METHODS: Subjects with scleromyositis from a prospective cohort were divided into three groups based on autoantibody profiles: subjects with SSc-specific autoantibodies (anti-centromere, -topoisomerase 1, -RNA polymerase III, -Th/To, -fibrillarin), subjects with SSc-overlap autoantibodies (anti-PM/Scl, -U1RNP, -Ku) and subjects without SSc-related autoantibodies. Clinical features, laboratory tests and histopathological findings were retrieved and compared between groups. RESULTS: Of 42 scleromyositis subjects (79% female, mean age at diagnosis 55 years, mean disease duration 3.5 years), 8 (19%) subjects had SSc-specific autoantibodies, 14 (33%) SSc-overlap autoantibodies and 20 (48%) had no SSc-related autoantibodies. One-third had no skin involvement, a finding more frequent in the SSc-overlap subjects and those without SSc-related autoantibodies. Proximal and distal weakness was common and head drop/bent spine was found in 50% of the SSc-specific and 35% of the subjects without SSc-related autoantibodies. Of note, the group without SSc-related autoantibodies had the only cases of severe cardiac systolic dysfunction (n = 1) and scleroderma renal crisis (n = 1), as well as three out of the four cancers and three out of the four deaths. CONCLUSION: In this carefully phenotyped series of scleromyositis subjects, absence of SSc-related autoantibodies was common and associated with distinct features and poor prognosis. Future studies are needed to validate these results and possibly identify novel autoantibodies or other biomarkers associated with scleromyositis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".