Clinical Implications of a Link between Fetal Alcohol Spectrum Disorder and Attention-Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the animal and human research literature on the link between fetal alcohol spectrum disorder (FASD) and attention-deficit hyperactivity disorder (ADHD). METHOD: We conducted a comprehensive literature review that addressed the history of, and current research on, fetal alcohol syndrome (FAS) and FASD, as well as that on ADHD in children. RESULTS: In animal and human research, there is emerging clinical, neuropsychological, and neurochemical evidence of a link between FASD and ADHD. CONCLUSIONS: The evidence of the link between these 2 conditions has implications for clinical management. The clinical quality of ADHD in children with FASD often differs from that of children without FASD. For children with FASD, ADHD is more likely to be the earlier-onset, inattention subtype, with comorbid developmental, psychiatric, and medical conditions. Children with FASD are commonly not mentally retarded but present complex learning disabilities, especially a mixed receptive-expressive language disorder with deficits in social cognition and communication (reminiscent of sensory aphasia and apraxia), working memory problems, and frequently, a mathematics disorder. Comorbid psychiatric conditions include anxiety, mood, conduct, or explosive disorders. As well, cardiac, renal, or skeletal problems are more likely to be present. Because these children have a disturbance in brain neurochemistry, or even brain structure (that is, in the corpus callosum), their response to standard psychostimulant medication can be quite unpredictable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".