Muscle Strength and Muscle Endurance During the First Year of Treatment of Polymyositis and Dermatomyositis: A Prospective Study
Bibliographic record
Abstract
Objective. To investigate muscle impairment (isometric and dynamic) and disease activity during the first year after diagnosis of polymyositis (PM) and dermatomyositis (DM), and to study the relationship between muscle impairment, patient-reported health, and disease activity. Methods. Seventy-two patients enrolled in the Swedish Myositis Register, 2003–2010, were followed prospectively. The Manual Muscle test (MMT-8; isometric muscle strength), the Functional Index of myositis test (FI-2; dynamic, repetitive muscle function), and disease activity (6-item core set) were retrieved at the time of diagnosis, and after 6 and 12 months. Self-reported health (Medical Outcomes Study Short Form-36; SF-36) was retrieved at 12 months. Results. At the time of diagnosis, median (Q1–Q3) for the FI-2 was 27.2% (7.9–60.5%) of maximal score compared to 93.8% (92.5–98.8%) of maximal MMT-8. At 12 months, the FI-2 and the MMT-8 improved to 29.4% (16.5–60.7%; p < 0.05) and 96.1% (88.1–99.4%), respectively (p < 0.01). At 12 months, 45% of patients improved ≥ 20%, and 27% worsened ≥ 20% in FI-2 score, while 10% improved ≥ 20% in MMT-8. Physician’s global visual analog scale (VAS), Health Assessment Questionnaire, and creatine phosphokinase levels improved significantly at 12 months (p < 0.05–0.001) while patient’s global and extramuscular VAS remained unchanged. The SF-36 physical function correlated strongly with the FI-2 (r s = 0.74; CI 0.55–0.85) and moderately with the MMT (r s = 0.54; CI 0.27–0.73), with lower correlations between muscle function and other SF-36 domains. Conclusion. Patients with PM/DM were characterized by impaired dynamic repetitive muscle function (DRMF) that correlated well with patient-reported physical function. Assessment of DRMF adds information regarding muscle impairment in these patients.
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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 | 0.000 | 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 | 0.001 |
| 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".