Precision and content range of a parent‐reported item bank assessing lower extremity and mobility skills in children with cerebral palsy
Bibliographic record
Abstract
AIM: The aim of this study was to determine the psychometric properties, content range, and measurement precision of a lower extremity physical functioning and mobility skills item bank (LE85) in children with cerebral palsy (CP). METHOD: Lower extremity functioning and mobility skill items were administered to 308 parents of children (169 males, 139 females; mean age 10y 8mo, SD 4y) with spastic CP (145 diplegia, 73 hemiplegia, 89 quadriplegia; [for one person type of CP was unknown]) classified using the Gross Motor Function Classification System (75 level I, 91 level II, 79 level III, 37 level IV, 26 level V). Additional legacy measures were administered to assess concurrent validity. Psychometric characteristics, differential item functioning, content range, and score precision were examined. RESULTS: The LE85 had acceptable psychometric properties. Content range matched the ability range of the sample population and exceeded legacy measures with minimal differential item functioning. The LE85 had good correlation with the Paediatric Outcomes Data Collection Instrument, Functional Independence Measure for Children, Gillette Functional Assessment Questionnaire, and Paediatric Quality of Life Inventory-CP module (range r=0.63-0.86). Precision of the LE85 and 10-item simulated computer adaptive test scores outperformed legacy measures. INTERPRETATION: The LE85 appears to be suitable to administer as a computer adaptive test to measure lower extremity physical functioning and mobility in children with 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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".