Motor learning in children with hemiplegic cerebral palsy and the role of sensation in short‐term motor training of goal‐directed reaching
Bibliographic record
Abstract
AIM: Our aim was to determine if improved upper limb kinematics in children with cerebral palsy (CP) during a reach-to-grasp task could be retained and transferred to a similar task. We also characterized the relationship between sensation and motor learning. METHOD: We used a prospective, single-participant research design with 16 children (seven males, nine females; mean/median age 8.6/9 y; age range 6-11 y) with spastic hemiparesis (Manual Ability Classification System levels II-IV). Children were randomly allocated to one of two groups: (1) task-oriented training with or (2) without trunk restraint. The intervention consisted of three 1-hour sessions per week for 5 weeks (total 15 h). Evaluations consisted of sensory modalities (tactile threshold, touch, proprioception, stereognosis) and upper limb kinematics during reach-to-grasp of an object located near and far from the body (five assessments: three pre-intervention, immediately post-intervention and 3 mo post-intervention). RESULTS: Motor improvements could be retained 3 months after the intervention and transferred to a similar task in children with CP. Proprioception and tactile thresholds were associated with retention of improvements in endpoint velocity (F2,13 =4.832, p=0.027). INTERPRETATION: Practice of activities aimed at improving upper limb kinematics led to better learning and retention of movement patterns in children with CP. Our results underline the importance of sensation for motor learning in children with CP.
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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) | 0.000 | 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".