Rehabilitation strategies to improve upper limb movement quality in children with cerebral palsy
Bibliographic record
Abstract
Children with CP are extremely heterogeneous in terms of etiology and clinical features. The diversity of symptoms among CP syndromes is a challenge for different branches of health research. Despite the efforts of many studies in examining rehabilitation strategies to improve upper limb (UL) function in children with CP, the confidence in the validity of these studies' evidence is still moderate to low. One limitation suggested is related to the type of outcomes used to measure improvement. Many are not sensitive enough to detect change (lack of responsiveness), are not age-related, and do not describe the movement quality. Movement quality concerns about movement performance or how well an activity is performed taking into reference normative data from typical populations. The assessment of movement quality in UL activities refers to the measurement of range of motion, hand trajectories, interjoint and intersegment coordination, muscle contraction patterns, and postural adjustments. The objective assessment of movement quality can be made by kinematic and kinetic analyses. The description of movement quality is important, because early brain injuries are more susceptible to 'maladaptative' plasticity, which might result in abnormal movement behaviors. The primary objective of this prospective single subject research design study was to determine the effect of two rehabilitation strategies in UL movement quality: arm constraint and trunk restraint, in the context of a modified constraint induced therapy (mCIT) and a task-oriented intervention, respectively. The UL movement quality was measured by kinematic analysis of a functional reaching task: a self-feeding simulation. Overall, the kinematic variables investigated are related to hand trajectories, arm angles and trunk forward displacement. Two clinical outcomes measuring UL movement quality were also used, the QUEST for the mCIT study, and the Melbou
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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) | 0.001 | 0.001 |
| 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".