Explanatory factors of dynamic balance impairment in myotonic dystrophy type 1
Bibliographic record
Abstract
INTRODUCTION/AIMS: Myotonic dystrophy type 1 (DM1) is a neuromuscular disease affecting many systems and for which muscle weakness is one of the cardinal symptoms. People with DM1 also present with balance-related impairments and high fall risk. The aim of this study was to explore explanatory factors of dynamic balance impairment in the DM1 population. METHODS: A secondary analysis of data collected as part of a larger study was performed. The Mini Balance Evaluation System Test (Mini-BESTest) was used to assess dynamic balance. Age, sex, and CTG repeat length in blood were retrieved from medical records and research files. The maximal isometric muscle strength of five lower limb muscle groups (hip flexors and extensors, knee flexors and extensors, and ankle dorsiflexors) was quantitatively assessed as well as fatigue. Standard multiple regression analysis was used. RESULTS: Fifty-two individuals (31 men) aged between 24 and 81 years were included. The final model explains 65.9% of the balance score; ankle dorsiflexor muscle strength was the strongest explanatory factor, followed by CTG repeat length, age and fatigue to a lesser extent. DISCUSSION: Dynamic balance is impaired in people with DM1. Results of this study suggest that rehabilitation interventions aimed at improving strength of the ankle dorsiflexors and managing fatigue could help to improve dynamic balance in this specific population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".