Robotic task specific training for upper limb neurorehabilitation: a mixed methods feasibility trial reporting achievable dose
Bibliographic record
Abstract
PURPOSE: Robotic devices for upper-limb neurorehabilitation allow an increase in intensity of practice, often relying on video game-based training strategies with limited capacity to individualise training and integrate functional training. This study shows the development of a robotic Task Specific Training (TST) protocol and evaluate the achieved dose. MATERIALS AND METHODS: Mixed-method study. A 3D robotic device for the upper limb, was made available to therapists for use during neurorehabilitation sessions. A first phase allowed clinicians to define a dedicated session protocol for TST. In a second phase the protocol was applied and the achieved dose was measured. RESULTS: = 10). Intervention duration: 4.5 ± 0.8 weeks, session frequency: 1.4 ± 0.2sessions/week, session length: 42 ± 9mins, session density: 39 ± 13%, intensity: 214 ± 84 movements/session, difficulty: dn = 0.77 ± 0.1 (normalised reaching distance) and Ɵ = 6.3 ± 23° (transverse reaching angle). Sessions' density and intensity were consistent across participants but clear differences of difficulty were observed. No changes in metrics were observed over the intervention. CONCLUSIONS: Robotic systems can support TST with high therapy intensity by modulating the practice difficulty to participants' needs and capabilities.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".