Clinical Heterogeneity of Patients With Antinuclear Matrix Protein 2 Antibody–Positive Myositis: A Retrospective Cohort Study in China
Bibliographic record
Abstract
OBJECTIVE: Heterogeneity exists among patients with myositis who have antinuclear matrix protein 2 (anti-NXP2) antibodies, although they usually present with severe muscle weakness. This study aimed to investigate the differences in phenotypes and prognoses among adult patients with myositis who have anti-NXP2 antibodies. METHODS: Adult patients with myositis who have anti-NXP2 antibodies were enrolled from January 2010 to December 2019. Their clinical features and laboratory data were recorded retrospectively. We followed up on their survival status until June 30, 2020. A hierarchical cluster analysis, Kaplan-Meier curves, and classification and regression trees were used to analyze the data. RESULTS: A total of 70 adult patients with myositis who have anti-NXP2 antibodies were enrolled. All patients experienced muscle weakness. A total of 11 patients did not present with rashes during disease progression, and 43 patients developed dysphagia. In total, 21 patients had interstitial lung disease (ILD), whereas no patients had rapidly progressive ILD. Hierarchical cluster analysis identified 2 clusters. Patients in cluster 1 were younger at disease onset, had a higher incidence of subcutaneous calcification, and had a lower incidence of V sign and shawl sign. Patients in cluster 2 had a higher frequency of ILD, accompanied by lower levels of lymphocytes and higher levels of serum ferritin. Moreover, patients in cluster 2 had worse prognoses. CONCLUSION: Patients with myositis who have anti-NXP2 antibodies may present with different phenotypes that are characterized by unique features and prognoses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".