Scoping review of outcome measures used in telerehabilitation and virtual reality for post-stroke rehabilitation
Bibliographic record
Abstract
Introduction Despite the increased interest in telerehabilitation (TR), virtual reality (VR) and outcome measures for stroke rehabilitation, surprisingly little research has been done to map and identify the most common outcome measures used in TR. For this review, we conducted a systematic search of the literature that reports outcome measures used in TR or VR for stroke rehabilitation. Our specific objectives included: 1) to identify the outcome measures used in TR and VR studies; and 2) to describe which parts of the International Classification of Functioning are measured in the studies. Methods We conducted a comprehensive search of relevant electronic databases (e.g. PubMed, the Cumulative Index to Nursing and Allied Health Literature, Embase, PSYCOINFO, The Cochrane Central Register of Controlled Trial and the Physiotherapy Evidence Database). The scoping review included all study designs. Two reviewers conducted pilot testing of the data extraction forms and independently screened all the studies and extracted the data. Disagreements about inclusion or exclusion were resolved by consensus or by consulting a third reviewer. Results In total, 28 studies were included in this scoping review. The results were synthesized and reported considering the implications of the findings within the clinical practice and policy context. Discussion This scoping review identified a wide range of outcome measures used in VR and TR studies and helped identify gaps in current use of outcome measures in the literature. The review also informs researchers and end users (i.e. clinicians, policymakers and researchers) regarding the most appropriate outcome measures for TR or VR.
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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 | 0.060 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".