Supporting client-centred task-oriented training by using low-cost motion detection technology adapted for use in neurological rehabilitation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction:\nClient-centred rehabilitation is important in people with central nervous system diseases\n(PwCNS) to regain or maintain functional ability in activities of daily life (ADL). In practice,\nrehabilitation services struggle to provide the optimal rehabilitation time of 6 hours per day.\nAs technology increases the patient’s motivation and adherence to therapy, the use of\nrehabilitation technology might increase rehabilitation time without decreasing the quality\nof therapy.\nObjectives:\nTo investigate the effect of an additional technology-based client-centred training on\nfunctional performance and ADL in PwCNS.\nMethod:\nA single-blinded randomised controlled trial was performed in PwCNS in 4 Belgian\nrehabilitation centres. The control group received conventional care. The intervention group\nreceived conventional care and additional training with a technology-based system during 6\nweeks, 3x/week, 45min/session. Assessments were performed at baseline, after 3 and 6\nweeks of training, and at 6-weeks follow-up. Primary outcome measures were Wolf Motor\nFunction Test, Manual Ability Measure-36 (MAM-36) and Canadian Occupational\nPerformance Measure.\nResults:\nA total of 45 PwCNS (age 59.07 ± 16.42) participated. Both control and intervention group\nimproved over time in all primary outcome measures. Improvement was mainly found\nduring the 6 week training period. Significant differences between groups was found\nregarding MAM-36 during training period, in favour of intervention group, and 6 weeks\nfollow-up period, benefitting the control group. Compliance to the intervention was 97.92%\nand no adverse effects of the intervention were reported.\n\nConclusion:\nThe additional training with an adapted technology-based system supports conventional\ncare and can be used to increase therapy time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it