Identifying the neurobehavioral phenotype of fetal alcohol spectrum disorder in young children.
Bibliographic record
Abstract
BACKGROUND: Most children with Fetal Alcohol Spectrum Disorder (FASD) do not display the typical facial changes, making the diagnosis much more challenging due to poor specificity of the brain dysfunction exhibited by these children. We have recently described and validated a behavioral phenotype of FASD using items from the Child Behavior Checklist (The Neurobehavioral Screening Test, NST). This tool has high sensitivity and specificity in separating children aged 6-13 yrs with FASD from those with ADHD and from healthy controls. OBJECTIVES: To test the validity of the NST for children aged 4-6 years in order to help facilitate diagnosis of FASD in young children. METHODS: Children referred to Motherisk for FASD diagnosis are all tested using the Child Behavior Checklist. We compared the scores of children 4-6 yrs diagnosed with FASD to those referred but not receiving a diagnosis, as well as to normal healthy control children of the same age range.ResultsOut of the 10 items of NST used at age 6-13 years, 3 are not scored in children 4-6 years of age. Using the 7 remaining items, children with FASD endorsed significantly more items (6.7+/-1.3) than healthy controls ( 2.3+/-1.2 ), or alcohol- exposed children who were not given an FASD diagnosis (4.7+/- 1.9). Using a cut-off of 5 out of7 items, the NST had a 94% sensitivity and 96% specificity in identifying children with FASD. Nine of 19 children exposed to alcohol with whom an FASD diagnosis could not be confirmed, scored 5 or more on the NST. CONCLUSIONS: In this pilot study, the NST has shown very high sensitivity and specificity and can be used to identify children who are very likely to be diagnosed with FASD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".