Post-stroke upper limb rehabilitation using virtual reality interventions: Do outcome measures assess extent or type of motor improvement?
Bibliographic record
Abstract
Post-stroke upper limb motor improvement continues to remain sub-optimal in a significant proportion of individuals sustaining a stroke. Efforts to enhance UL motor improvement have led to the use of evidence-based interventions including virtual reality technology. The effects of interventions on motor impairments, activity limitations and participation restrictions are commonly assesses using clinical outcomes. Majority of the clinical outcomes focus on the extent of motor improvement (i.e. how much). Information on the type (i.e. how) of recovery can be obtained by using a selected few clinical outcomes and movement pattern kinematic measures. The study objective was to characterize the outcomes used to assess the effects of virtual reality interventions in terms of quantifying the extent and type of upper limb motor improvement. We reviewed the published literature on the effects of virtual reality (VR) based interventions to enhance UL motor improvements. Outcomes from the retrieved studies were initially classified under the appropriate International Classification of Functioning categories. We then categorized the outcomes into those quantifying into type or extent of motor improvement based on existing evidence. We found 100 papers that investigated the effects of virtual reality interventions to enhance post-stroke UL motor improvement. Forty two different outcome measures were used across the 100 studies. Seventeen different outcomes assessed impairments, 16 were used to measure activity limitations and 6 measured participation restrictions and the effects of contextual factors. The Fugl Meyer Assessment, Wolf Motor Function Test and Stroke Impact Scale were most commonly used across the three categories. Of the retrieved 100 studies, 48 used an outcome that considered the type of recovery. Although a smaller proportion, 17 studies included outcomes of movement patterns. The use of outcomes considering the type of recovery is steadily increasing in studies using VR for post-stroke UL rehabilitation.
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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 | 0.038 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".