Age-related differences in neuropsychological assessment of fetal alcohol spectrum disorder: a cross-sectional study
Bibliographic record
Abstract
This cross-sectional study examined 6 key areas of neuropsychological functioning (cognitive, academic, attention, executive function, adaptive skills) comparing adolescents and school-age children with prenatal alcohol exposure (PAE). The aims were: (i) to examine which neuropsychological measures were predictive of an FASD diagnosis in adolescents and school-age children with PAE, and (ii) to compare the neuropsychological performance of adolescents and children diagnosed with FASD. Hierarchical logistic regressions determined that the Full-Scale IQ, Verbal Comprehension and Perceptual Reasoning indices, basic reading and math skills, adaptive functioning at school, and components of executive functioning (dependent on age) improved the probability of an accurate FASD diagnosis in both groups: 9.1% to 19.2% for adolescents and 10.9% to 19.4% for school-age children (61.5%-80.9% correct classifications overall). For the age comparison analyses (ANOVAs/MANOVAs), a significant difference was observed in the cognitive domain, as well as with basic math skills (trend) in the sample diagnosed with FASD, with lower scores observed for adolescents across these measures. These findings provide further evidence for age differences in neuropsychological assessment as well as increased neuropsychological difficulties in adolescence by comparison with childhood with FASD. Longitudinal studies will be needed to make further inferences about developmental changes in neuropsychological functioning in FASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".