Prenatal Exposures, Diagnostic Outcomes, and Life Experiences of Children and Youths with Fetal Alcohol Spectrum Disorder
Bibliographic record
Abstract
Children and youths diagnosed with FASD may experience a range of adverse health and social outcomes. This cross-sectional study investigated the characteristics and outcomes of children and youths diagnosed with FASD between 2015 and 2018 at the Sunny Hill Centre in British Columbia, Canada and examined the relationships between prenatal substance exposures, FASD diagnostic categories, and adverse health and social outcomes. Patient chart data were obtained for 1187 children and youths diagnosed with FASD and analyzed. The patients (mean age: 9.7 years; range: 2–19) had up to 6 physical and 11 mental health disorders. Prenatal exposure to other substances (in addition to alcohol) significantly increased the severity of FASD diagnosis (OR: 1.18): the odds of FASD with sentinel facial features (SFF) were 41% higher with prenatal cigarette/nicotine/tobacco exposure; 75% higher with exposure to cocaine/crack; and two times higher with exposure to opioids. Maternal mental health issues and poor nutrition also increase the severity of FASD diagnosis (60% and 6%, respectively). Prenatal exposure to other substances in addition to alcohol significantly predicts involvement in the child welfare system (OR: 1.52) and current substance use when adjusted for age (aOR: 1.51). Diagnosis of FASD with SFF is associated with an increased number of physical (R2 = 0.071, F (3,1183) = 30.51, p = 0.000) and mental health comorbidities (R2 = 0.023, F (3,1185) = 9.51, p = 0.000) as compared to FASD without SFF adjusted for age and the number of prenatal substances. Screening of pregnant women for alcohol and other substance use, mental health status, and nutrition is extremely important.
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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 | 0.000 | 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 | 0.000 |
| 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".