Instruments and Parameters for Evaluating Upper Limb Motor Fatigability in Individuals with Neuromuscular Diseases: Systematic Review
Bibliographic record
Abstract
Background. Neuromuscular diseases present a set of clinical and pathological disabilities that include muscle weakness and atrophy, perception of fatigue, fatigability, and contracture. Motor fatigability compromises the ability of the individual to generate muscle strength and perform their daily activities. Quantitative measures of strength and motor fatigability are important to determine the clinical progression of the disease and the response to the proposed treatments. Thus, the aim of this study was to identify the equipment and protocols frequently used to assess upper limb motor fatigability in patients with neuromuscular disease. Methods. Information such as equipment used to induce motor fatigability, body segment or joint studied, movement analyzed, type of contraction, and protocol utilized for the test was analyzed. Joanna Briggs Scale and Newcastle-Ottawa Scale assessed the methodological quality of the studies. In addition, a checklist was prepared by the research group to assess the protocols presented in the referred studies. Results. The isokinetic and handgrip dynamometers were the most utilized equipment to induce motor fatigability. 83% of the studies had a design with low methodological rigor and half of them with high risk of bias. In the analysis of the protocols utilized to induce motor fatigability, one study was classified as regular and the other ones as good. Conclusion. The methodological topics to assess motor fatigability were incompletely described considering the electrophysiological and biomechanical approach. Although the motor fatigability in the upper limb was evaluated using isokinetic and handgrip equipment, the absence of a gold standard protocol still compromises the understanding of clinical progression and responses to the treatments in the neuromuscular diseases. This trial is registered with CRD42021206934.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".