The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability
Bibliographic record
Abstract
The Manual Ability Classification System (MACS) has been developed to classify how children with cerebral palsy (CP) use their hands when handling objects in daily activities. The classification is designed to reflect the child's typical manual performance, not the child's maximal capacity. It classifies the collaborative use of both hands together. Validation was based on the experience within an expert group, a review of the literature, and thorough analysis of children across a spectrum of function. Discussions continued until consensus was reached, first about the constructs, then about the content of the five levels. Parents and therapists were interviewed about the content and the description of levels. Reliability was tested between pairs of therapists for 168 children (70 females, 98 males; with hemiplegia [n= 52], diplegia [n= 70], tetraplegia [n= 19], ataxia [n= 6], dyskinesia [n= 19], and unspecified CP [n= 2]) between 4 and 18 years and between 25 parents and their children's therapists. The results demonstrated that MACS has good validity and reliability. The intraclass correlation coefficient between therapists was 0.97 (95% confidence interval 0.96–0.98), and between parents and therapist was 0.96 (0.89-0.98), indicating excellent agreement.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".