Prevalence of Autism in Children Prenatally Exposed to Alcohol: A Pilot Study
Bibliographic record
Abstract
Abstract BACKGROUND: The prevalence of autism among Canadian children is 1.1 in 100.1 The possibilty of an association between prenatal alcohol exposure and autism was based on early case reports describing autistic behaviour in children with Fetal Alcohol Syndrome.2 OBJECTIVES: The objective of this study was to identify the prevalence of autism in children prenatally exposed to alcohol. DESIGN/METHODS: A chart review was conducted of 220 children, aged 4 to 16 years, admitted to a Fetal Alcohol Spectrum Disorder(FASD) Clinic, and prenatally exposed to alcohol. Descriptive statistics were used to determine prevalence of the autism diagnosis,number of children diagnosed with FASD, and relevant demographic data. RESULTS: Nine (9) of the total 220 children, or 4.1 %,had been diagnosed with autism using standarized tests. Mean age was 7.6 years with a range of 4.3 to 14.2 years. Five (5) of the children were boys, and 4 were girls. Six (6) additional children had autistic behaviours: autism was ruled out in 2 of the 6 children, and 4 had not been assessed. Eight (8) of the 9 children diagnosed with autism were also diagnosed with FASD, and in 1 child the diagnosis was deferred. CONCLUSION: The prevalence of autism among children prenatally exposed to alcohol was significantly higher than the overall Canadian population (4.1 % vs. 1.1 %). Implications for practice and research are described.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".