The use of functional electrical stimulation to improve upper limb function in children with hemiplegic cerebral palsy: A feasibility study
Bibliographic record
Abstract
Background Grasping and manipulating objects are common problems for children with hemiplegic cerebral palsy. Multichannel-functional electrical stimulation may help facilitate upper limb movements and improve function. Objective To evaluate the feasibility of multichannel-functional electrical stimulation to improve grasp and upper limb function in children with hemiplegic cerebral palsy to inform the development of a clinical trial. Methods A prospective pre-/post-test/follow-up (six months) design with three children, aged 6–13 years, was used. Multichannel-functional electrical stimulation (mFES) was applied to the hemiplegic upper limb for up to 48 sessions over 16 weeks. Feasibility indicators included recruitment of participants and adherence rates, safety, and discomfort/pain. Effectiveness was assessed using the grasp domain of the Quality of Upper Extremity Skills Test, and other secondary clinical outcome measures with “success” criteria set a priori. Results Participant recruitment target was not met but adherence was high, and multichannel-functional electrical stimulation was found to be safe and comfortable. Of the three participants, two improved in grasp at post-test, whereas one child’s ability deteriorated. Only one child met success criteria on most outcomes at post-test. Conclusions Feasibility indicators met success criteria, except for participant recruitment. Treatment effectiveness was mixed. A future case comparison investigation with a larger but more selected sample is suggested.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".