Validation of manual muscle testing and a subset of eight muscles for adult and juvenile idiopathic inflammatory myopathies
Bibliographic record
Abstract
OBJECTIVE: To validate manual muscle testing (MMT) for strength assessment in juvenile and adult dermatomyositis (DM) and polymyositis (PM). METHODS: Patients with PM/DM (73 children and 45 adults) were assessed at baseline and reevaluated 6-9 months later. We compared Total MMT (a group of 24 proximal, distal, and axial muscles) and Proximal MMT (7 proximal muscle groups) tested bilaterally on a 0-10 scale with 144 subsets of 6 and 96 subsets of 8 muscle groups tested unilaterally. Expert consensus was used to rank the best abbreviated MMT subsets for face validity and ease of assessment. RESULTS: The Total, Proximal, and best MMT subsets had excellent internal reliability (Total MMT r(s) = 0.91-0.98), and consistency (Cronbach's alpha = 0.78-0.97). Inter- and intrarater reliability were acceptable (Kendall's W 0.68-0.76, r(s) = 0.84-0.95). MMT subset scores correlated highly with Total and Proximal MMT scores and with the Childhood Myositis Assessment Scale, and correlated moderately with physician global activity, functional disability, magnetic resonance imaging, and axial and distal MMT scores, and, in adults, with creatine kinase level. The standardized response mean for Total MMT was 0.56 in juveniles and 0.75 in adults. Consensus was reached to use a subset of 8 muscles (neck flexors, deltoids, biceps, wrist extensors, gluteus maximus and medius, quadriceps, and ankle dorsiflexors) that performed as well as the Total and Proximal MMT, and had good face validity and ease of assessment. CONCLUSION: These findings aid in standardizing the use of MMT for assessing strength as an outcome measure for myositis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".