Using Assistive Robotic Technology in Motor Neurorehabilitation After Childhood Stroke
Bibliographic record
Abstract
An intracranial haemorrhage resulted in severe motor impairment in the right upper limb in a previously physically-active young adolescent. Robot-assisted therapy was implemented over the course of 9 months to reduce motor impairment in the affected right upper limb. Robot-assisted therapy consisted of twice weekly sessions containing repetitive and progressively intense practice of hand grasping and arm reaching. The amount of assistance offered by the robot during grasping and reaching was adaptive such that as impairment reduced, the robot offered less assistance. Motor impairment was evaluated with the Fugl-Meyer assessment score for the upper limb. The robot-assisted therapy was associated with an increase in a clinical outcome measure of motor impairment following childhood stroke. The increase was distributed across both upper and lower arm segments. The final, stable reduction in motor impairment in the patient matched that demonstrated in a group of adult unilateral stroke survivors receiving similar treatment with robotics early in their recovery process. W e conclude that robot-assisted therapy offers a promising treatment option in childhood stroke involving severe upper limb motor impairment. Robot-assisted therapy could be implemented safely in an acute in-patient hospital unit and be continued after discharge to the community setting. doi:10.4021/jnr98w
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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".