The Prevalence of Individual Histopathologic Features Varies according to Autoantibody Status in Muscle Biopsies from Patients with Dermatomyositis
Bibliographic record
Abstract
Objective. Individual dermatomyositis (DM)-associated autoantibodies are associated with distinct clinical phenotypes. This study was undertaken to explore the association of these autoantibodies with specific muscle biopsy features. Methods. DM subjects with a muscle biopsy reviewed at Johns Hopkins had sera screened for autoantibodies recognizing Mi-2, transcriptional intermediary factor 1-γ (TIF1-γ), NXP2, MDA5, Ro52, PM-Scl, and Jo1. We also included anti-Jo1–positive patients with polymyositis (PM) who had a biopsy read at Johns Hopkins. Analyzed histological features included perifascicular atrophy, perivascular inflammation, mitochondrial dysfunction, primary inflammation, and myofiber necrosis. Duration of disease, biopsy location, and treatment at biopsy were also analyzed. Results. We studied 91 DM and 7 anti-Jo1–positive patients with PM. In univariate analyses, TIF1-γ+ patients had more mitochondrial dysfunction (47% vs 18%; p = 0.05), NXP2+ patients had less primary inflammation (0% vs 28%; p = 0.01), Mi-2+ patients had more primary inflammation (50% vs 19%; p = 0.03), and PM-Scl+ patients had more primary inflammation (67% vs 18%; p = 0.004) than those who were negative for each autoantibody. Although reliability was limited because of small sample numbers, multivariate analysis confirmed that TIF1-γ+ patients had more mitochondrial dysfunction [prevalence ratio (PR) 2.6, 95% CI 1.0–6.5, p = 0.05] and PM-Scl+ patients had more primary inflammation (PR 5.2, 95% CI 2.0–13.4; p = 0.001) independent of disease duration at biopsy, biopsy site, and treatment at biopsy. No differences in muscle biopsy features were noted between anti-Jo1–positive patients diagnosed with DM and PM. Conclusion. The prevalence of different histological features varies according to autoantibody status in DM. Muscle biopsy features are similar in anti-Jo1 patients with and without a rash.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".