Association between maternal cannabis use and birth outcomes: an observational study
Bibliographic record
Abstract
BACKGROUND: As cannabis consumption is increasing globally, including among pregnant women, there is a critical need to understand the effects of cannabis on fetal development and birth outcomes. We had two objectives: to determine 1) the factors associated with self-reported cannabis use in the pre/early-pregnancy period, and 2) whether cannabis use is associated with low birth weight, preterm birth, or small size for gestational age (GA) infants. METHODS: Maternal questionnaire and birth outcome data was gathered from 2229 women and 1778 singleton infants in the Ontario Birth Study, a hospital-based prospective cohort study (2013-2019). Women self-reported cannabis use within 3 months of learning their pregnancy status. Multivariable linear and logistic regression was conducted to 1) identify factors associated with cannabis use, and 2) determine the associations between cannabis use with the selected birth outcomes. RESULTS: Cannabis use increased in the cohort over time. Women who reported cannabis use (N = 216) were more likely to be younger and more likely to use alcohol, tobacco, and prescription pain medication, although most did not. These women had infants born at lower average birth weights and had 2.0 times the odds of being small for GA (95% confidence interval: 1.3, 3.3) after multivariable adjustment for socioeconomic factors and other substance use. CONCLUSION: Our results suggest that women who use cannabis around the time of conception have higher odds of having infants that are small for gestational age. Targeted clinical messaging may be most applicable to women actively trying to conceive.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".