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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".