Clinical and Serologic Correlates of Anti‐PM/Scl Antibodies in Systemic Sclerosis: A Multicenter Study of 763 Patients
Bibliographic record
Abstract
OBJECTIVE: Anti-PM/Scl autoantibodies are found in polymyositis, dermatomyositis, systemic sclerosis (SSc), and systemic autoimmune disease overlap syndromes. PM-1α is a major epitope of the PM/Scl complex, and antibodies against PM-1α can be detected using a validated enzyme-linked immunosorbent assay (ELISA). This study was undertaken to determine the prevalence and identify the clinical correlates of anti-PM-1α antibodies in a large cohort of patients with SSc. METHODS: Serum samples were obtained from 763 patients with SSc enrolled in a multicenter Canadian cohort. The sera were analyzed by ELISA for the presence of antibodies against PM-1α. Associations between the presence of anti-PM-1α antibodies and demographic, clinical, and other serologic manifestations of SSc were investigated. RESULTS: Anti-PM-1α antibodies were present in 55 patients with SSc (7.2%), of whom almost 50% (26 of 55; 3.4% of the overall cohort) had no other SSc-specific antibodies, namely anticentromere, anti-topoisomerase I, and anti-RNA polymerase III. Features positively associated with the presence of anti-PM-1α antibodies included younger age at disease onset, skeletal muscle involvement, calcinosis, inflammatory arthritis, and overlap disease. Interstitial lung disease was less frequent and there were fewer gastrointestinal symptoms present in patients with anti-PM-1α antibodies compared to patients without these antibodies. CONCLUSION: Anti-PM-1α antibodies are relatively common in SSc and are associated with a distinct clinical phenotype, consistent with that described in association with other anti-PM/Scl autoantibodies. Although anti-PM-1α antibodies are not exclusive of other SSc-specific antibodies, they can be present in the absence thereof. Thus, anti-PM-1α antibodies may have considerable diagnostic and prognostic relevance in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".