Identifying fetal alcohol spectrum disorder among South African children at aged 1 and 5 years
Bibliographic record
Abstract
BACKGROUND: Fetal Alcohol Spectrum Disorders (FASD) are a global health concern. Early intervention mitigates deficits, yet early diagnosis remains challenging. We examined whether children can be screened and meet diagnoses for FASD at 1.5 years compared to 5 years post-birth. METHODS: A population cohort of pregnant women in 24 neighborhoods (N = 1258) was recruited and 84.5 %-96 % were reassessed at two weeks post-birth, 0.5 years, 1.5 years, 3 years, and 5 years later. A two-step process was followed to diagnose FASD; first, a paraprofessional screened the children and then a physician evaluated the child. We evaluated FASD symptoms at 1.5 vs. 5 years. We also examined maternal differences in children receiving a positive FASD screening (n = 160) with those who received a negative FASD screening. RESULTS: Screening positive for FASD more than doubled from 1.5 years to 5 years (from 6.8 % to 14.8 %). About one quarter of children who screened positive and were evaluated by a physician, were diagnosed as having a FASD. However, half did not complete the 2nd stage screening. Compared to mothers of children with a negative FASD screening, mothers of children with a positive FASD screening were less likely to have a high school education and more likely to have lower incomes, have experienced interpersonal partner violence, and have a depressed mood. Mothers of children who did not follow up for a 2nd stage physician evaluation were more like to live in informal housing compared to those who followed-up (81.3 % vs. 62.5 %, p = 0.014). CONCLUSIONS: We found that children can be screened and diagnosed for FASD at 1.5 and 5 years. As FASD characteristics develop over time, repeated screenings are necessary to identify all affected children and launch preventive interventions. Referrals for children to see a physician to confirm diagnosis and link children to care remains a challenge. Integration with the primary healthcare system might mitigate some of those difficulties.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".