A differential approach for examining the behavioural phenotype of fetal alcohol spectrum disorders.
Bibliographic record
Abstract
BACKGROUND: In 2006, Nash and colleagues published results suggesting that individual items from the Child Behavior Checklist (CBCL) could be used as a screening tool that was highly sensitive in differentiating children with FASD from controls and children with Attention Deficit Hyperactivity Disorder (ADHD). Since many of the items referred to features of Oppositional Defiant/Conduct Disorder (ODD/CD), it was not clear whether the items reflected comorbidity with ODD/CD, or were unique to children with FASD. OBJECTIVES: The present study sought to replicate the results of our 2006 paper using a new and larger sample, which also includes a group of children diagnosed with ODD/CD. METHODS: Retrospective psychological chart review was conducted on 56 children with FASD, 50 with ADHD, 60 with ODD/CD, and 50 normal control (NC) children. Receiver operating characteristic curve (ROC) analysis of CBCL items discriminating FASD from NC was used to compare FASD to the ADHD and ODD/CD groups. RESULTS: ROC analyses showed scores of a) 3 or higher on 10 items differentiated FASD from NC with a sensitivity of 98%, specificity of 42% and b) 2 or higher on 5 items reflecting oppositional behaviors differentiated FASD from ADHD with a sensitivity of 89% and specificity of 42%. CONCLUSION: Our findings partially replicate the results of our 2006 study and additionally elucidate the behavioural differences between children with FASD and those with ODD/CD. The proposed screening tool is currently the only tool available that is empirically derived and able to differentiate children with FASD from children with clinically similar profiles.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".