Comparison of strength testing modalities in dysferlinopathy
Bibliographic record
Abstract
INTRODUCTION/AIMS: Dysferlinopathy demonstrates heterogeneity in muscle weakness between patients, which can progress at different rates over time. Changing muscle strength due to disease progression or from an investigational product is associated with changing functional ability. The purpose of this study was to compare three methods of strength testing used in the Clinical Outcome Study (COS) for dysferlinopathy to understand which method and which muscle groups were most sensitive to change over time. METHODS: Patients were evaluated at each study visit using functional scales, manual muscle testing, and handheld dynamometry (HHD) at all 15 sites. A fixed-frame system (Fixed) was used at a subset of seven sites. Screening and baseline visits were evaluated for reliability. Data over a 1-year period were analyzed to determine sensitivity to change among strength modalities and individual muscle groups. RESULTS: HHD and Fixed captured significant change across 1 year in summed muscle strength score of four muscle groups (P < .01). Strength summed scores were significantly correlated with functional scales (rho = 0.68-0.92, P < .001). Individual muscle groups, however, showed high levels of variability between visits. DISCUSSION: Although both HHD and Fixed demonstrate change over 12 months, HHD is a less expensive option that provides data on a continuous scale and may be easier to implement. Due to variability in strength measures, researchers should carefully consider use of strength testing as an outcome and may wish to select functional measures with less variability as clinical trial endpoints.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".