Estimating the Prevalence of Fetal Alcohol Spectrum Disorder in Australia
Bibliographic record
Abstract
INTRODUCTION: Fetal alcohol spectrum disorder (FASD) is caused by prenatal alcohol exposure (PAE) and characterised by severe neurodevelopmental impairment. Australian studies have reported PAE prevalence of between 14% and 78% of births. Estimating national FASD prevalence in the general population using gold-standard active case ascertainment is costly and time-consuming, and alternative approaches are required. METHODS: Using a published equation for the risk of FASD following PAE (estimated from an international meta-analysis) and a pooled estimate of PAE prevalence in Australia (from a meta-analysis of 78 studies reporting 16 large general population-based birth cohorts between 1975 and 2018), we estimated the population prevalence of FASD. Monte Carlo simulations were used to determine confidence intervals. RESULTS: Estimated FASD prevalence in the general population was 3.64% (95% confidence interval 2.91%, 4.41%). DISCUSSION AND CONCLUSIONS: The estimated FASD prevalence in the general population of Australia was comparable to that in other high-income countries (e.g., USA, Canada). Although it is likely that certain vulnerable populations have significantly higher FASD prevalence, this estimate provides a baseline estimate for the general population to inform service development and strategies for prevention of FASD and guide future research.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".