Wristful thinking: exploring the effects of robotic rehabilitation and cross-education for persons with multiple sclerosis
Bibliographic record
Abstract
The goal of this work was to develop an adaptive rehabilitation technique using a haptic wrist robot that would induce cross-education to an untrained limb. Fourteen individuals with Multiple Sclerosis (MS) and eight non-affected adults completed an eight-week intervention. MS participants were placed into two groups, training their more affected limb (direct training), and training their less affected limb (indirect training). The purpose of the intervention was to improve wrist and grip strength (measured via maximal grip and isometric wrist strength) and motor control (measured via robotic assessments). Participants trained with the robotic device three times per week for eight consecutive weeks. Training consisted of eccentric contractions as the participant resisted a force elicited from the robotic device as it moved in flexion, extension, and radial-ulnar deviation. The MS group reported significant increases in wrist strength. The indirect training group significantly improved in flexion, extension, radial and ulnar deviation in the trained limb, and flexion and radial deviation in the untrained limb. The direct training group showed improvements in extension and ulnar deviation in both limbs. The control group improved in radial and ulnar deviation, with radial deviation improving in the untrained limb. Grip force remained unchanged for all groups. MS participants significantly decreased tracking and figural error post-intervention suggesting evidence that motor control adaptations occurred following an adaptive and resistive robotic intervention of the upper limb. Results of this work provide evidence that eight-week robotic rehabilitation can elicit cross-education effects to the untrained limb.
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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 | 0.001 |
| 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".