Classification of manual abilities in children with cerebral palsy under 5 years of age: how reliable is the Manual Ability Classification System?
Bibliographic record
Abstract
OBJECTIVE: To assess the interobserver reliability of the Manual Ability Classification System (MACS) in young children (age 1-5 years) with cerebral palsy. DESIGN: Interobserver reliability study. SETTING: A cross-sectional study of a hospital-based population of children with cerebral palsy. SUBJECTS: Thirty children, 18 boys and 12 girls between 1 and 5 years of age (mean age 2.5 years +/- 14.2 SD, Gross Motor Function Classification System level I-IV). MEASURES: the children were classified by means of the MACS by two independent observers. Interobserver reliability was analysed using Cohen's kappa. RESULTS: Overall interobserver reliability of the MACS for children aged 1-5 years was moderate, with a linear weighted kappa (kappa) of 0.62 (95% confidence interval (CI) 0.49-0.76). According to the generally accepted categories of agreement, reliability was moderate for children under 2 years of age (kappa = 0.55), and good for children between 2 and 5 years of age (kappa = 0.67). CONCLUSION: Classification of manual ability of young children with cerebral palsy is possible between 2 and 5 years of age. For children younger than 2 years old, it should be done with caution. Further development of the MACS for children under 5 years of age is recommended with an emphasis on age-appropriate descriptions of manual abilities.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".