Screening approaches for identifying fetal alcohol spectrum disorder in children, adolescents, and adults: A systematic review
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorder (FASD) is a prevalent neurodevelopmental disorder that is caused by prenatal alcohol exposure (PAE) and associated with a range of cognitive, affective, and health concerns. Although the identification of FASD can facilitate the provision of interventions and support, and plays a protective role against adverse outcomes, there are high rates of missed detection. The identification of FASD via screening may improve its recognition across settings. The current systematic review examined the available evidence on FASD screening tools and approaches across age groups and settings. METHODS: A systematic search was carried out for both peer-reviewed studies and gray literature sources published between January 1990 and May 2020 and was preregistered with PROSPERO (#CRD42019122077). Studies included in the review focused on human applications of FASD screening in children, adolescents, and adults. The quality of the studies was assessed using the QUADAS-2 and GRADE frameworks. RESULTS: The search yielded 20 screening tools and approaches across 45 studies, broadly characterized in 2 groups. The first group included approaches currently in use that aim to identify individuals at risk of FASD using a range of markers (n = 19) or associated sentinel dysmorphic facial features (n = 6). Another group of studies, characterized as emerging, focused on identifying promising biomarkers of PAE/FASD (n = 20). Overall, we identified limited research supporting the psychometric properties of most screening approaches. The quality review provided evidence of bias due to the common use of case-control designs and lack of adequate reference standards. CONCLUSIONS: Although several FASD screening tools and approaches are available for use across a range of age groups and settings, the overall evidence base supporting their psychometric properties is weak, with most studies demonstrating significant risk of bias. Service providers should exercise caution in selecting and implementing FASD screening tools given these limitations. It is critically important to accurately identify individuals with FASD across ages and settings to support healthy outcomes. Thus, there is a pressing need for additional research in this area, particularly validation studies in large and representative samples using robust methodological approaches.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".