Investigating cross-education in multiple sclerosis following upper limb robotic rehabilitation
Bibliographic record
Abstract
The overall goal of this thesis was to develop an adaptive rehabilitation technique using a haptic wrist robotic device that would induce the phenomenon of cross-education to improve upper limb function. Following a literature review, Chapter 4 provides a scoping review of existing literature surrounding rehabilitation robots for MS. Chapter 5 reports the development and rationale of the rehabilitative approach, to develop an algorithm that is individualized and adaptive to the user. Once the algorithm was developed, an eight-week intervention for fourteen individuals with MS and eight non-affected adults was conducted. The purpose of the intervention was two-fold and presented in Chapter 6, to improve overall wrist and grip strength (assessed via maximal grip and isometric wrist strength) and Chapter 7, to improve overall motor control (assessed via robotic performance measures). Lastly, in both chapters, cross-over effects of strength and motor control to the untrained limb were evaluated. Results of this eight-week training reported increases in wrist strength for the MS group with an average percent change score across all muscle directions of 62.59% in the trained limb and 53.26% in the untrained limb. The control group also reported an average percent change of 31.31% in strength in the trained limb and 24.26% in the untrained limb. MS participants significantly decreased in tracking and figural error (degree of error) post-intervention suggesting evidence that motor control adaptations are possible following an adaptive and resistive robotic intervention of the upper limb. Following the results of the eight-week intervention, Chapter 9 was to investigate changes in strength and motor control following four-weeks of training in an additional subgroup of participants following the same rehabilitative protocol. Results demonstrate a clinically meaningful improvement in strength of the trained limb, but no significant improvements in figural or tracking error performance – suggestive that robotic rehabilitation of this kind needs to be longer in duration than four-weeks to elicit meaningful adaptations of motor control.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".