A Population-Based Study on Women Who Used Alcohol during Pregnancy and Their Neonates in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Data from birth registries can be studied to assess the prevalence of prenatal alcohol use and associated maternal and neonatal outcomes. METHODS: Linked maternal and neonatal data (2015-2018) for alcohol-exposed pregnancies were obtained from the Better Outcomes Registry and Network (BORN) Ontario. Descriptive statistics were generated for maternal demographics, prenatal substance use, mental health/substance use history, and neonatal outcomes. Logistic regression models were performed to assess the odds of prenatal heavy (binge or weekly) alcohol and other substance use based on mental health/substance use history and other maternal demographics, and the impacts of heavy alcohol use and other prenatal substance exposures on neonatal outcomes. RESULTS: A total of 10,172 (2.4%) women reported alcohol use during pregnancy. One-third had pre-existing or current mental health and/or substance use problems, which was associated with significantly higher odds of heavy alcohol use during pregnancy. Prenatal exposure to heavy alcohol use was associated with increased odds of neonatal abstinence syndrome (2.5 times); respiratory distress syndrome (2.3 times); neonatal intensive care unit (NICU) admission (58%); and hyperbilirubinemia (57%). Prenatal exposure to one or more substances in addition to alcohol was associated with significantly higher odds of fetal/maternal/placental pregnancy complications; preterm birth; NICU admission; low APGAR scores; one or more confirmed congenital anomalies at birth; respiratory distress syndrome; and intrauterine growth restriction. CONCLUSIONS: It is crucial to routinely screen childbearing-age and pregnant women for alcohol and other substance use as well as mental health problems in order to prevent adverse maternal and neonatal outcomes.
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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".