Validating the ICF core set for cerebral palsy using a national disability sample in Taiwan
Bibliographic record
Abstract
Purpose: To validate the activities and participation (d) codes of two age-specific brief International Classification of Functioning, Disability, and Health (ICF) core sets for school-aged children with cerebral palsy (CP), using national dataset of the child version of the Functioning Scale of the Disability Evaluation System (FUNDES) in Taiwan.Methods: Students with CP aged 6–17.9 years (n = 546) in the national dataset were analyzed. Items of the child version of the FUNDES were linked to the ICF d-codes and matched to two brief ICF core sets for CP. The restriction rate of the linked d-codes were calculated. Random Forest regression was applied to select the important linked d-codes for predicting school participation frequency.Results: The vast majority of the content of the Taiwanese dataset was covered by two core sets. The matched d-codes represent high restriction rates (80%) and most were important for predicting school participation. One important code, d740 (formal relationships, such as relationship with teachers), identified in this study were not included in two ICF core sets.Conclusions: Two brief ICF core sets for CP capture the majority of relevant functional information collected by the child version of the FUNDES. Some additional codes not covered in the international ICF core sets should be considered for inclusion in the revised Taiwanese version.Implications for rehabilitationCerebral palsy (CP) is the most common cause of severe physical disability in childhood. ICF core sets for CP promote a comprehensive assessment and service provision.To ensure applicability, ICF core sets for CP were validated in Taiwan using the child and youth national dataset of the child version of the Functioning Scale of the Disability Evaluation System. This study shows content validity and proposes new ICF codes additions for the Taiwanese version.Among top five ICF-based predictors for school participation frequency, four of them were consistent in both children and youth groups as d310–d350 (basic communication), d750 (informal social relationships), d820 (school education), and d710–d720, d880 and d920 (social play), which could be taken into consideration in clinical application.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".