Motor skills and intelligence in children with fetal alcohol spectrum disorder
Bibliographic record
Abstract
AIM: To determine the relationship between motor abilities and intelligence in children and young people with prenatal alcohol exposure (PAE) being assessed for fetal alcohol spectrum disorder (FASD). METHOD: This was a cross-sectional correlational study of children and young people with PAE being assessed for FASD. The relationship between motor abilities (Movement Assessment Battery for Children, Second Edition) and intelligence (Wechsler Intelligence Scale for Children, Fourth or Fifth Edition) was calculated using correlation and regression analyses. Attention and executive function were considered as potential confounding variables. RESULTS: The relationship between motor abilities and intelligence in 73 children and young people (48 males, 25 females; aged 6-17y, mean age 10y 5mo [SD 2y 9mo]) assessed for FASD was small and statistically non-significant (r=0.05, p=0.67). INTERPRETATION: The findings confirm that motor abilities and intelligence should be assessed separately when investigating an FASD diagnosis. Intelligence scores should not be used to estimate motor abilities, nor should they dictate when motor testing be completed. Assessing intelligence and motor domains separately will enhance diagnostic accuracy, identify the need for strategies or interventions to address functional motor skills, and further define the role of physiotherapy and occupational therapy in FASD assessment and intervention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".