Occupational therapy using a robotic-assisted glove ameliorates finger dexterity and modulates functional connectivity in amyotrophic lateral sclerosis
Bibliographic record
Abstract
INTRODUCTION: Although rehabilitation is recommended for amyotrophic lateral sclerosis (ALS), improvement of functional decline has hardly been achieved. We investigated the effect of occupational therapy that uses a robotic-assisted glove (RAG) on hand dexterity and the functional connectivities found in the brain of ALS patients. METHOD: Ten patients diagnosed with ALS and admitted to the Shiga University of Medical Science (SUMS) Hospital from December 2018 to December 2021 participated in the study. These participants chose the hand side to wear RAG and exercised for two weeks. A sham movement was performed on the other side. We administered several functional assessments, including the Simple Test for Evaluating Hand Function (STEF), grip strength, pinch meter for grip strength, Canadian occupational performance measure (COPM), as well as nerve conduction study (NCS) before and after the exercise, and evaluated the results. We also analyzed six patients' resting-state functional magnetic resonance imaging (rs-fMRI). RESULTS: Two-week robotic rehabilitation improved the STEF, grip strength, and COPM scores when compared with those of the other side. However, no significant effect was observed in the pinch meter and the NCS results. The rs-fMRI data analysis revealed that the robotic rehabilitation augmented two functional connectivities between the left pallidum-right supplementary motor cortex and right insular cortex-right sensorimotor network among the patients, which had beneficial effects. CONCLUSION: The occupational therapy using RAG displayed improved hand dexterity. The enhanced functional connectivities around the sensorimotor network might be associated with the improvement in hand dexterity because of the RAG.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".