Construct validity of the Capacity Profile in preschool children with cerebral palsy
Bibliographic record
Abstract
The Capacity Profile (CAP) classifies additional care needs, subdivided into five domains of body functions (physical health, motor, sensory, mental, and voice/speech) of children with stable conditions. Construct validity of the CAP was established in 72 children (56 males, 16 females) with cerebral palsy (CP); median age 2 years 7 months, range 2 years 6 months to 3 years; 34 unilateral and 37 bilateral spastic-type CP, one dyskinetic-type CP. Gross Motor Function Classification System (GMFCS) classification was 24 in level I, eight in level II, 18 in level III, 14 in level IV, and eight in level V. All CAP domains were significantly associated (p<0.001) with the Functional Skills (rho=-0.42 to -0.85) and Caregiver Assistance scales (rho=-0.42 to -0.82) of the Dutch Paediatric Evaluation of Disability Inventory. The CAP-motor domain and GMFCS were strongly correlated (rho=0.91, p<0.001). Stepwise regression analysis demonstrated that the CAP domains contributed 74% to mobility (CAP-motor 66%, mental 6%, voice 2%); 75% to self-care (CAP-voice 61%, mental 12%, physical 2%); and 70% to social functionality (CAP-mental 68%, voice 2%). CAP demonstrated good construct validity in young children with CP. The independent contribution of CAP domains to daily function underscores the importance of comprehensive assessment with regard to all domains of body functions in heterogeneous conditions like CP.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".