Recognising the spectrum of scleromyositis: HEp-2 ANA patterns allow identification of a novel clinical subset with anti-SMN autoantibodies
Bibliographic record
Abstract
OBJECTIVE: To describe systemic sclerosis (SSc) with myopathy in patients without classic SSc-specific and SSc-overlap autoantibodies (aAbs), referred to as seronegative scleromyositis. METHODS: Twenty patients with seronegative scleromyositis diagnosed by expert opinion were analysed retrospectively for SSc features at myositis diagnosis and follow-up, and stratified based on HEp-2 nuclear patterns by indirect immunofluorescence (IIF) according to International Consensus of Autoantibody Patterns. Specificities were analysed by protein A-assisted immunoprecipitation. Myopathy was considered an organ involvement of SSc. RESULTS: SSc sine scleroderma was a frequent presentation (45%) at myositis diagnosis. Myositis was the most common first non-Raynaud manifestation of SSc (55%). Lower oesophagal dysmotility was present in 10 of 11 (91%) investigated patients. At follow-up, 80% of the patients met the American College of Rheumatology/EULAR SSc classification criteria. Two-thirds of patients had a positive HEp-2 IIF nuclear pattern (all with titers ≥1/320), defining three novel scleromyositis subsets. First, antinuclear antibody (ANA)-negative scleromyositis was associated with interstitial lung disease (ILD) and renal crisis. Second, a speckled pattern uncovered multiple rare SSc-specific aAbs. Third, the nuclear dots pattern was associated with aAbs to survival of motor neuron (SMN) complex and a novel scleromyositis subset characteriszed by calcinosis but infrequent ILD and renal crisis. CONCLUSIONS: SSc skin involvement is often absent in early seronegative scleromyositis. ANA positivity, Raynaud phenomenon, SSc-type capillaroscopy and/or lower oesophagal dysmotility may be clues for scleromyositis. Using HEp-2 IIF patterns, three novel clinicoserological subsets of scleromyositis emerged, notably (1) ANA-negative, (2) ANA-positive with a speckled pattern and (3) ANA-positive with nuclear dots and anti-SMN aAbs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".