Fine motor recovery in chronic stroke: Commercial gaming in community level care
Bibliographic record
Abstract
Introduction: In the years that follow a stroke, many survivors are living with persisting upper extremity deficits that can affect their ability to complete activities of daily living. Chronic stroke survivors are often offered little to no rehabilitation due to a perceived motor plateau, which can affect their motivation and confidence towards future rehabilitation. Virtual reality, in the form of commercial gaming, can act as a new and motivating way to complete rehabilitation, and can be a time and cost effective tool for the survivor. Purpose: To investigate the effectiveness of commercial video gaming as an intervention for fine motor recovery in chronic stroke. Methods: Seven participants in the chronic phase (i.e., one year) post-stroke have completed an eight-week program in which they played the Nintendo Wii for 15 minutes two times per week using their more affected hand as part of their larger rehabilitation program. The outcome measures used were the Stroke Impact Scale (SIS). Jebsen Hand Function Test (JHFT), Box and Block Test (BBT), and the Nine Hole Peg Test (NHPT). Results: Percent change scores from baseline through post-testing show improvements in the more affected hand on all four dependent measures. SIS scores have improved an average of 8.1%, JHFT scores have improved 10.3%, BBT scores by 22.3% and NHTP scores have improved by 23.9%. Discussion: These encouraging findings within this limited sample showcase the positive impact commercial gaming can have on those with chronic stroke in a community rehabilitation setting and suggest that further study is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".