Technology-Enabled Upper Limb Rehabilitation for Neurological Impairment: A Feasibility Randomized-Controlled Trial Protocol
Bibliographic record
Abstract
Background. Technology-enabled upper limb (UL) rehabilitation improves therapy intensity and impairment outcomes, however clinical usage remains low and evidence for functional outcomes is limited. While benefits of single-mode technologies have been demonstrated, a combination of technology modes or “hybrid” approach is an emerging option with shoulder to fingertip capability. Investigation of this approach within a hospital setting is warranted to inform occupational therapy practice with neurological patients. Purpose. This study examines feasibility of hybrid technology-enabled UL rehabilitation for in-patients with neurological impairments. Method and Analyses. A Phase II feasibility randomized controlled trial (RCT) will compare usual care versus hybrid technology intervention using three technology modes (robotics, virtual reality, sensor-based therapy) plus usual care. Pre-post outcomes for UL impairment, activity, participation and self-reported function will be analyzed using 2 × 2 repeated measures ANOVA. Effect sizes will inform a power analysis for a full-scale RCT. Field observations and participant surveys will capture feasibility factors. It is anticipated hybrid technology for UL neurorehabilitation will be feasible in a hospital setting and show preliminary effectiveness for improving UL use in daily activities. Ethics and Dissemination. Ethics granted from RBWH Human Research Ethics Committee (HREC/2020/QRBW/67076) and The University of Queensland (2021/HE002211).
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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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".