Supporting client-centred task-oriented training by using low-cost motion detection technology adapted for use in neurological rehabilitation.
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".