Street drug use during pregnancy: potential programming effects on preschool wheeze
Bibliographic record
Abstract
Street drug use during pregnancy is detrimental to fetal development. Although the prevalence of wheeze is high in offspring of substance-abusing mothers, nothing is known about the role of street drug use during pregnancy in its development. We investigated the impact of maternal street drug use and distress during pregnancy on the development of wheeze and allergy in preschool children. Questionnaire data were accessed from the Community Perinatal Care trial of 791 mother-child pairs in Calgary, Alberta. Using logistic regression, the association between maternal substance use and distress during pregnancy, and wheeze and allergy at age 3 years was determined in boys and girls. After adjusting for alcohol use during pregnancy, pre- and postnatal tobacco use, preterm birth, duration of exclusive breastfeeding, daycare attendance and maternal socioeconomic status, maternal street drug use during pregnancy [odds ratio (OR): 5.02, 95% confidence interval (CI): 1.30-19.4] and severe maternal distress during pregnancy (OR: 5.79, 95% CI: 1.25-26.8) were associated with wheeze in girls. In boys, an independent association was found between severe distress during pregnancy (OR: 3.85, 95% CI: 1.11-13.3) and allergies, but there was no association with maternal street drug use. In conclusion, we found an association between maternal street drug use and wheeze in preschool girls that could not be accounted for by maternal distress, smoking or alcohol use during pregnancy. Prenatal programming effects of street drugs may explain this association.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".