The Effectiveness of Using the HandTutor Hand Simulator and Stabilometric Postural Control Using the Biofeedback Tear in Preschool and Primary School Children with Cerebral Palsy
Bibliographic record
Abstract
Background. Currently, mechanotherapy is widely used in the rehabilitation of mainly adult patients and adolescents. The possibilities of using this technique in children of early, preschool and primary school age are limited due to the minimal presence of specialized devices. The aim of the study is the assessment of the effectiveness of a comprehensive rehabilitation program with the inclusion of a HandTutor hand simulator and stabilometrical postural control using the biofeedback method in preschool and primary school age children suffering from infantile cerebral palsy (ICP). Materials and methods. 40 children aged 3 to 7 years with motor disorders with a diagnosis of ICP were under observation. The effectiveness of the program was evaluated using scales and hardware measurement data (HandTutor, “Amblyotron”). Results. According to goniometric changes and data on the Ashworth scale, after rehabilitation, patients demonstrate a greater range of movements than before treatment (p < 0.05). The indicators of the area of the pressure center and its fluctuations have significantly improved in the form of improved stability (p £ 0.01), the pattern of vertical posture, walking and postural control in general. Conclusion. After treatment, patients showed a marked decrease in the degree of muscle spasticity of the affected limb, an increase in muscle strength, an improvement in fine and large hand motor skills, and manual skills. Thus, the results of the study demonstrate the high effectiveness of biomechanical methods of influence, namely HandTutor and “Amblyotron”, on the motor sphere of patients with severe motor disorders.
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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) | 0.000 | 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".