Latent classes of neurodevelopmental profiles and needs in children and adolescents with prenatal alcohol exposure
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorder (FASD) resulting from prenatal alcohol exposure (PAE) is a common neurodevelopmental disorder, but substantial interindividual heterogeneity complicates timely and accurate assessment, diagnosis, and intervention. The current study aimed to identify classes of children and adolescents with PAE assessed for FASD according to their pattern of significant neurodevelopmental functioning across 10 domains using latent class analysis (LCA), and to characterize these subgroups across clinical features. METHODS: Data from the Canadian National FASD Database, a large ongoing repository of anonymized clinical data received from diagnostic clinics across Canada, was analyzed using a retrospective cross-sectional cohort design. The sample included 1440 children and adolescents ages 6 to 17 years (M = 11.0, SD = 3.5, 41.7% female) with confirmed PAE assessed for FASD between 2016 and 2020. RESULTS: Results revealed an optimal four-class solution. The Global needs group was characterized by high overall neurodevelopmental impairment considered severe in nature. The Regulation and Cognitive needs groups presented with moderate but substantively distinguishable patterns of significant neurodevelopmental impairment. The Attention needs group was characterized by relatively low probabilities of significant neurodevelopmental impairment. Both the Global and Regulation needs groups also presented with the highest probabilities of clinical needs, further signifying potential substantive differences in assessment and intervention needs across classes. CONCLUSIONS: Four relatively distinct subgroups were present in a large heterogeneous sample of children and adolescents with PAE assessed for FASD in Canada. These findings may inform clinical services by guiding clinicians to identify distinct service pathways for these subgroups, potentially increasing access to a more personalized treatment approach and improving outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".