Accuracy of motor assessment in the diagnosis of fetal alcohol spectrum disorder
Bibliographic record
Abstract
BACKGROUND: To evaluate the accuracy of motor assessment tools listed in Fetal alcohol spectrum disorder: a guideline for diagnosis across the lifespan (Canadian Guideline) for the purpose of fetal alcohol spectrum disorder (FASD) diagnosis. Specifically, we aimed to determine: 1) diagnostic accuracy of motor assessment tools and subtests; 2) accuracy of multiple subtests versus total scores; and 3) accuracy of alternate cut-offs. METHODS: Cross-sectional diagnostic study of 63 children aged 6-17 years. Diagnostic accuracy and alternate cut-offs were calculated for the Movement Assessment Battery for Children, 2nd edition (MABC-2), Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition Short Form (BOT-2SF) and Beery-Buktenica Developmental Test of Visual Motor Integration, 6th edition (BeeryVMI-6). RESULTS: The MABC-2 total motor score was more sensitive (0.30; 95% CI 0.17-0.46; p < 0.01) to motor impairment in the presence of FASD than the BOT-2SF (0.02; 95% CI 0.00-0.12) at the 2nd percentile (-2SD). The MABC-2 total motor score was more accurate than any combination of subtest scores. The Motor Coordination subtest of the BeeryVMI-6 (BeeryMC) at the 5th percentile (- 1.5SD) (sensitivity 0.68, specificity 0.90) was the most accurate subtest. CONCLUSIONS: The BOT-2SF was an inaccurate assessment tool for FASD diagnosis. The MABC-2 total motor score was the most accurate using current guidelines, though its sensitivity was still low. Further investigation into inclusion of single subtests and/or using a less conservative cut-off in the Canadian Guideline is warranted.
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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 | 0.001 | 0.001 |
| 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".