Relationships between Lower Limb Muscle Strength Impairments and Physical Limitations in DM1
Bibliographic record
Abstract
BACKGROUND: Although adult and late-onset DM1 phenotypes DM1 present distinct lower limb weaknesses portraits, resulting physical limitations have never been described separately for each phenotype. OBJECTIVE: To characterize the lower limb weaknesses and physical limitations among the DM1 adult and late-onset phenotypes separately and to document the contribution of weaknesses on mobility to optimize the management of this population. METHODS: The strength of four muscle groups among 198 participants was quantified. Participants were categorized according to the severity of their muscular involvement using the Muscular Impairment Rating Scale (MIRS). Physical limitations were assessed using the Timed up-and-go (TUG), Berg Balance Scale (BBS) and 10 meters comfortable walking speed (10MWT). Multiple linear regressions were performed to identify the contribution of each muscle group to the mobility tests scores. RESULTS: Late-onset demonstrated less weakness and physical limitations (p < 0.001 - 0.002) than the adult phenotype, but 21.9-47.5% of participants with this phenotype showed mobility scores below reference values. Physical limitations were observed in the first two MIRS grades (37.5-42.1% of the participants) for the TUG and 10MWT. Ankle dorsiflexors and knee extensors were the two muscle groups that showed the strongest relationships with mobility scores. CONCLUSION: Although less impaired, the late-onset phenotype shows significant lower limb muscle weakness associated with physical limitations. The surprising presence of quantitative lower limb muscle weakness in the first two MIRS grades needs to be considered when using this scale. Both ankle dorsiflexors and knee extensors appear to be good indicators of physical limitations in DM1.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".