Use of a visual biofeedback in the movement patterns recovery by patients with the central paresis
Bibliographic record
Abstract
We evaluated the effectiveness of two virtual reality therapies (VRT) with visual biofeedback, Armeo Spring® upper limb exoskeleton (Armeo) and Homebalance® interactive system (Homebalance), in early post-stroke rehabilitation. Using a randomized controlled study design, participants within 30 days after stroke with arm paresis (Armeo study) or with balance problem (Homebalance study) were assigned either to the respective intervention group (Armeo IG n=25; mean age 66.5 years, and Homebalance IG n=25; mean age 69.6 years) performing VRT instead of conventional physiotherapy or to the control group (Armeo CG, n=25, mean age 68.1 years, and Homebalance CG, n=25, mean age 65.9 years) having conventional physiotherapy only. Montreal Cognitive Assessment (MoCA), Functional Independence Measure (FIM), Fugl Mayer Assessment-Upper Extremity Scale (FMA-UE), Modified Rivermead Mobility Index (m-RIM) and Berg Balance Scale (BBS) were performed before and after the 3-week therapy with 12 therapies. Results of participants <65 and ≥65 years old were compared. Acceptance of both VRTs was evaluated by self-rated questionnaire. In the Armeo study, paretic upper arm function improved significantly in both IG and CG groups, the improvement in FMA-UE was significantly higher in Armeo IG as compared to CG (p=0.02)...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".