Fetal alcohol syndrome: a prospective national surveillance study
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of cases of fetal alcohol syndrome (FAS) seen by Australian paediatricians. METHODS: Active, national case-finding using the Australian Paediatric Surveillance Unit (APSU). Monthly reporting of incident cases aged <15 years by paediatricians between January 2001 and December 2004. RESULTS: Over 1150 paediatricians submitted reports each month to the APSU. Of 169 reported cases, 92 fulfilled the study criteria for FAS. There was a significant increase in the number of children reported each year from 2001 to 2004. Of 92 children, 53.3% were male, 35.7% were preterm (<37 weeks' gestation) and 64.6% were of low birth weight (<2.5 kg). Most (94.4%) had high risk exposure to alcohol in utero and 78.3% were exposed to one or more additional drugs. The median age at diagnosis was 3.3 years (range: newborn to 11.9 years): 6.5% were diagnosed at birth and 63% by 5 years of age. Of the 92 cases, 56% had growth deficiency, 53.2% had microcephaly, 85.9% had evidence of central nervous system dysfunction, 24% had additional birth defects, 5.4% had sensorineural deafness and 4.3% had visual impairment. Of children with FAS, 65% were Indigenous, 51% had a sibling with FAS, and only 40.2% lived with a biological parent. CONCLUSION: Our data are the only prospective national data available on FAS throughout the world. These findings highlight the severity, complexity and impact of FAS, the need for effective strategies for prevention, and the necessity for education to facilitate earlier diagnosis, referral and reporting of cases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".