Development and psychometric evaluation of performance-based upper extremity motor control test (PB-UE-MCT) for people with cerebral palsy
Bibliographic record
Abstract
Abstract Introduction Upper extremity disorders limits the performance in the activities of daily living, especially, in bilateral (two-handed) activities. Objective This study was designed to develop a performance-based upper extremity motor control test (PB-UE-MCT) and to measure its psychometric properties (including, convergent validity, intrarater reliability, and interrater reliability) for people with cerebral palsy (CP). Method The PB-UE-MCT was developed in three phases, including planning, construction, and psychometric evaluation. The participants included 50 people with CP with an age range of 6 to 18 years. To measure internal consistency, Cronbach's alpha was run (n=50). Intrarater and interrater reliability was measured for 25 participants. To assess convergent validity, the correlations of the PB-UE-MCT with the Manual Ability Classification System (MACS) and with the Selective Control Upper Extremity Scale (SCUES) were calculated. Results The values obtained for Cronbach's alpha (.90 to .96) indicated the excellent internal consistency of the PB-UE-MCT. The ICC values for intrarater reliability and interrater reliability were between .84 and .99 and between .89 and .99, respectively. The correlation coefficients obtained for the items of the PB-UE-MCT and those of the MACS were between .51 and .73. The correlation coefficients of the items of the PB-UE-MCT with those of the SCUES were in the range of .67 to .98, which proves the PB-UE-MCT's good to excellent levels of convergent validity. Conclusion The results confirm that the PB-UE-MCT is a valid and reliable tool for evaluating the upper extremity performance of people with CP through task analysis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".