Factors predictive of a fetal alcohol spectrum disorder: Neuropsychological assessment
Bibliographic record
Abstract
A variety of neurodevelopmental impairments related to fetal alcohol spectrum disorder (FASD) diagnoses have been consistently documented. However, it is not clear whether such variables are predictive of a diagnosis. The purpose of the present study is to use logistic regressions to identify predictors of FASD in neuropsychological assessment. Charts of 180 children and adolescents with prenatal alcohol exposure (PAE) who underwent psychological and diagnostic assessment for FASD were retrospectively reviewed. A total of 107 received an FASD diagnosis (the PAE-FASD group) and 73 did not (the PAE group). Following preliminary analyses, direct logistic regressions were performed to assess the contribution of different neuropsychological testing measures on the likelihood of a child or adolescent receiving an FASD diagnosis. The results indicate that the classification accuracy of the PAE-FASD and PAE groups is clinically significant across models of intelligence, academic achievement, memory, and executive functioning. Classification rates across the various models range from 67.1% to 75.5%, with models incorporating 10 intelligence subtests or 3 academic subtests emerging as superior to those using broad indices of intelligence and/or individual subtests of memory or executive functioning. A "test battery" model incorporating verbal intelligence, verbal/auditory working memory (digit span), basic reading and spelling skills, math calculations, delayed story recall, and spatial planning and problem-solving yielded a classification rate of 74.7%. These results suggest that neuropsychological testing is a critical component of FASD assessment and help guide decisions to maximize the efficiency and efficacy of the diagnostic process and treatment recommendations.
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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 | 0.008 |
| 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 | 0.000 |
| 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".