Facilitating person-centred task-oriented training with a human-centred developed rehabilitation technology in neurorehabilitation
Bibliographic record
Abstract
Background\nFor persons with central nervous system diseases (PwCNS) a person-centred task-\noriented rehabilitation approach seems important to regain or maintain functional\nability in daily life activities (ADL). However, rehabilitation services struggle to provide\nthis approach and to provide the optimal rehabilitation time of 6 hours per day.\nRehabilitation technology has proven to increase the person’s motivation and\nadherence to therapy. The use of rehabilitation technology may also be able to\nincrease rehabilitation time without decreasing the quality of therapy or increasing the\ntherapists’ workload.\nAim\nTo investigate the effect of additional person-centred task-oriented training with a\ncustomised rehabilitation technology on functional performance and ADL in PwCNS\nand whether individualised goals are more explicitly trained in the intervention group.\nMethods and materials\nA multicentre single-blinded randomised controlled trial was performed in PwCNS.\nThe control group received treatment-as-usual. The intervention group received\ntreatment-as-usual and additional training with a customised technology during 6\nweeks, 3x/week, 45min/session under supervision of a trained professional.\nAssessments were performed at baseline, after 3 and 6 weeks of training, and at 6\nweeks follow-up. The primary outcome measures were Wolf Motor Function Test,\nManual Ability Measure-36 (MAM-36) and Canadian Occupational Performance\nMeasure (COPM). Additionally, the trained and untrained goals of both groups were\ncompared to investigate whether the individualised goals were more explicitly trained\nin the intervention group.\nResults\nForty-five PwCNS (age 59.07 ± 16.42) performed the full protocol. Both the control\nand intervention group improved over time on the primary outcome measures, mainly\nduring the 6-week training period. Significant differences between control and\nintervention group were found regarding MAM-36 after 6 weeks of training in favour\nof the intervention group. In the control group, the distribution of untrained versus\ntrained COPM goals was about 50%. While in the intervention group, more than 85%\n\nof the COPM goals were implemented in the treatment-as-usual and additional\nintervention programme.\nConclusions\nAdditional training with a customised rehabilitation technology can enhance\ntreatment-as-usual and may facilitate a person-centred task-oriented approach in\nPwCNS. This intervention might be used to increase therapy time in the future but\nresearch into independent use by PwCNS is necessary.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".