Epilepsy as a Comorbidity in Polymyositis and Dermatomyositis—A Cross-Sectional Study
Bibliographic record
Abstract
Polymyositis (PM) and dermatomyositis (DM) are autoimmune-mediated multisystemic myopathies, characterized mainly by proximal muscle weakness. A connection between epilepsy and PM/DM has not been reported previously. Our study aim is to evaluate this association. A case–control study was conducted, enrolling a total of 12,278 patients with 2085 cases (17.0%) and 10,193 subjects in the control group (83.0%). Student’s t-test was used to evaluate continuous variables, while the chi-square test was applied for the distribution of categorical variables. Log-rank test, Kaplan–Meier curves and multivariate Cox proportional hazards method were performed for the analysis regarding survival. Of the studied 2085 cases, 1475 subjects (70.7%) were diagnosed with DM, and 610 patients (29.3%) with PM. Participants enrolled as cases had a significantly higher rate of epilepsy (n = 48 [2.3%]) as compared to controls (n = 141 [1.4%], p < 0.0005). Using multivariable logistic regression analysis, PM was found only to be significantly associated with epilepsy (OR 2.2 [95%CI 1.36 to 3.55], p = 0.0014), whereas a non-significant positive trend was noted in DM (OR 1.51 [95%CI 0.99 to 2.30], p = 0.0547). Our data suggest that PM is associated with a higher rate of epilepsy compared to controls. Physicians should be aware of this comorbidity in patients with immune-mediated myopathies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".