Reliability of kinematic measures of functional reaching in children with cerebral palsy
Bibliographic record
Abstract
AIM: The determination of rehabilitation effectiveness in children with cerebral palsy (CP) depends on the metric properties of the outcome measure. We evaluated the reliability of kinematic measures of functional upper limb reaching movements in children with CP. METHOD: Thirteen children (ten females, three males) with spastic hemiplegic, diplegic, or quadriplegic CP affecting at least one arm (mean age 9y, SD 1.6y; range 6-11y; Manual Ability Classification System [MACS] levels II-IV) were evaluated three times over 5 weeks. The kinematics of the more affected arm reaching to grasp a 2cm(3) block placed at three distances from the body midline were analysed. The reliability (test-retest) of six kinematic variables (endpoint trajectory straightness and smoothness, trunk displacement, elbow extension, shoulder horizontal adduction, and shoulder flexion] was tested and expressed as intraclass correlation coefficients (ICC, model 2,K) and 95% confidence intervals. RESULTS: Trajectory smoothness, trunk displacement, elbow extension, and shoulder flexion (far target) had the highest ICCs (0.82-0.95). Other kinematic variables had moderate (0.50< or =ICC< or =0.81) or low (0.17-0.38) reliability. Test-retest reliability was task dependent, as reaches required different degrees of trunk displacement and joint excursion. INTERPRETATION: Kinematic variables can be used as outcomes in clinical trials to test upper limb intervention effectiveness on motor performance and movement quality. As kinematic variables are task specific, reliability should be interpreted in the context of task requirements.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".