Outcome Measures in Tele-Rehabilitation and Virtual Reality for Stroke Survivors: Protocol for a Scoping Review
Bibliographic record
Abstract
UNLABELLED: Despite the increased interest about tele-rehabilitation, virtual reality and outcome measures for stroke rehabilitation, surprisingly little research has been done to map and summarize the most common outcome measures used in tele-rehabilitation. For this review, we propose to conduct a systematic search of the literature that reports outcome measures used in tele-rehabilitation or virtual reality for stroke rehabilitation. Specific objectives include: 1) to identify the outcome measures used in tele-rehabilitation studies; 2) to describe the psychometric properties of the outcome measures in the included studies; 3) to describe which parts of the International Classification of Functioning are measured in the studies. METHODS: we will conduct a comprehensive search of relevant electronic databases (e.g., PUBMED, CINAHL, EMBASE, PSYCOINFO, Cochrane Central Register of Controlled Trial and PEDRO). The scoping review will include all study designs. Two reviewers will pilot-test the data extraction forms and will independently screen all the studies and extract the data. Disagreements about inclusion or exclusion will be resolved by consensus or by consulting a third reviewer. The results will be synthesized and reported considering the implications of the findings within the clinical practice and policy context. Dissemination: we anticipate that this scoping review will contribute to inform researchers and end-users (ie, clinicians and policy-makers), regarding the most appropriate outcome measures for tele-rehabilitation or virtual reality as well as help to identify gaps in current measures. Results will be disseminated through reports and open access journals, conference presentations, as well as newsletters, podcasts and meetings targeting all the relevant stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.075 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.013 |
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".