Population-based prevalence of fetal alcohol spectrum disorder in Canada
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorder (FASD) is one of the most disabling potential outcomes of prenatal alcohol exposure. The population-based prevalence of FASD among the general population of Canada was unknown. The objective of this study was to determine the population-based prevalence of FASD among elementary school students, aged 7 to 9 years, in the Greater Toronto Area (GTA) in Ontario, Canada. METHODS: This screening study used a cross-sectional, observational design utilizing active case ascertainment, along with retrospective collection of prenatal alcohol exposure information. Data collection involved two phases. Phase I consisted of taking growth measurements, a dysmorphology examination, and obtaining a history of behavioral and/or learning problems. Phase II consisted of a neurodevelopmental assessment, maternal interview, and behavioral observations/ratings by parents/guardians. Final diagnostic screening conclusions were made by consensus by a team of experienced multidisciplinary experts during case conferences, using the 2005 Canadian guidelines for FASD diagnosis. The prevalence of FASD was estimated, taking into consideration the selection rate, which was used to account for students who dropped out or were lost to follow-up during each phase. Monte Carlo simulations were employed to derive the confidence interval (CI) for the point estimates. RESULTS: A total of 2555 students participated. A total of 21 cases of suspected FASD were identified. The prevalence of FASD was estimated to be 18.1 per 1000, or about 1.8%. Using a less conservative approach (sensitivity analysis), the prevalence of FASD was estimated to be 29.3 per 1000, or about 2.9%. Therefore, the population-based prevalence of FASD is likely to range between 2 and 3% among elementary school students in the GTA in Ontario, Canada. CONCLUSIONS: This study provides the first population-based estimate of the prevalence of FASD in Canada. The estimate is approximately double or possibly even triple previous crude estimates. FASD prevalence exceeds that of other common birth defects such as Down's syndrome, spina bifida, trisomy 18, as well as autism spectrum disorder in Canada. More effective prevention strategies targeting alcohol use during pregnancy, surveillance of FASD, and timely interventions and support to individuals with FASD and their families are urgently needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".