Development of a parent‐report computer‐adaptive test to assess physical functioning in children with cerebral palsy II: upper‐extremity skills
Bibliographic record
Abstract
The specific aims of this study were to (1) examine the psychometric properties (unidimensionality, differential item functioning, scale coverage) of an item bank of upper-extremity skills for children and adolescents with cerebral palsy (CP); (2) evaluate a simulated computer-adaptive test (CAT) using this item bank; (3) examine the concurrent validity of the CAT with the Pediatric Outcomes Data Collection Instrument (PODCI) upper-extremity core scale; and (4) determine the discriminant validity of the simulated CAT with Manual Ability Classification System (MACS) levels and CP type (i.e. diplegia, hemiplegia, or quadriplegia). Parents (n=180) of children and adolescents with CP (spastic diplegia 49%, hemiplegia 22%, or quadriplegia 28%) consisting of 102 males and 78 females with a mean age of 10 years 6 months (SD 4y 1mo, range 2-21y), and MACS levels I through V participated in calibration of an item pool and completed the PODCI. Confirmatory factor analyses supported a unidimensional model using 49 of the 53 upper-extremity items. Simulated CATs of 5, 10, and 15 items demonstrated excellent accuracy (intraclass correlation coefficient [ICCs] >0.93) with the full item bank, had high correlations with the PODCI upper-extremity core scale score (ICC 0.79), and discriminated among MACS levels. The simulated CATs demonstrated excellent overall content coverage over a wide age span and severity of upper-extremity involvement. The future development and refinement of CATs for parent report of physical function in children and adolescents with CP is supported by our work.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".