Validation of the fine motor subtest of the Bayley‐III with children with sickle cell disease using Rasch analysis
Bibliographic record
Abstract
BACKGROUND: Children with sickle cell disease (SCD) are at risk for fine motor (FM) delays; however, screening for FM impairments is not common among young children with SCD. The Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) is the most commonly used performance-based developmental assessment. We aim to determine if the FM subtest of the Bayley-III is structured hierarchically in accordance with development and comprehensively evaluates FM development in children with SCD. METHODS: Bayley-III assessments were completed between October 2009 and December 2013. The Bayley-III FM screening test, a shorter and more rapid method of assessing for FM impairments, was not directly administered to participants. Screening test scores were calculated from full Bayley-III scores. RESULTS: Rasch analysis was performed using WINSTEPS. Sixty children with SCD were included in the final Rasch model. The Rasch-generated Wright map, which jointly positions items and persons on the same latent trait, illustrated that the FM items were slightly skewed towards more challenging items, indicating more difficult items may be overrepresented. High item separation values were reported (17.4), and item outfit statistics were less than 1.7. More than one third of items demonstrated overfit, indicating possible item redundancy. The FM subtest and the screening test, a shorter and faster method of assessing skills, were highly correlated (r = 0.993, p < 0.001). CONCLUSION: The Bayley-III FM subtest is structured hierarchically, aligning with motor development, and comprehensively evaluates FM development in children with SCD. The test could be improved by reordering items, removing overfitting items and modifying screening test items to capture all ranges of development. The screening test is comprehensive and has high potential clinical utility among children with SCD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".