Cannabis use among pregnant women under different legalization frameworks in the United States
Bibliographic record
Abstract
Background: Cannabis use in pregnancy is associated with adverse neonatal outcomes, yet its use among pregnant women in the United States has increased significantly.Objectives: This cross-sectional study explored how cannabis use in pregnant women varied between different cannabis legalization frameworks, that is, permitted use of cannabidiol (CBD)-only, medical cannabis, and adult-use cannabis.Methods: Behavioral Risk Factor Surveillance System data from 2017 to 2020 was utilized with respondents classified by their state’s policies into CBD-only, medical, and adult-use groups. Outcome measures included prevalence of use and usage characteristics (frequency, method of intake, and reason for use) among pregnant women. Logistic regression models were estimated to evaluate the association between legal status and prevalence of use.Results: The unweighted dataset included 1,992 pregnant women. Recent cannabis use was reported by (weighted proportions): 2.4% (95%CI: 0–4.4) of respondents in the CBD-only group, 7.1% (95%CI: 4.0–10.1) in the medical group and 6.9% (95%CI: 3.0–10.9) in the adult-use group. Compared to the CBD-only group, respondents in the medical and adult-use groups were 4.5-fold (adjusted; 95%CI: 1.4–14.7; p = .01) and 4.7-fold (adjusted; 95%CI: 1.3–16.2; p = .02) more likely to use cannabis. Across all groups, smoking was the most common method of intake and over 49% of users reported using partially or entirely for adult-use purposes.Conclusions: The increased use with legalization motivates further research on the impacts of cannabis as a therapeutic agent during pregnancy and supports the need for increased screening and patient counseling regarding the potential effects of cannabis use on fetal development.
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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 | 0.003 |
| 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 | 0.000 | 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".