The prevalence and significance of gestational cannabis use at an Australian tertiary hospital
Bibliographic record
Abstract
BACKGROUND: Cannabis is one of the most common non-prescribed psychoactive substances used in pregnancy. The prevalence of gestational cannabis use is increasing. AIM: The aim was to examine the prevalence of gestational cannabis use and associated pregnancy and neonate outcomes. MATERIALS AND METHODS: A retrospective observational study involving pregnant women delivering in 2019 was conducted at a tertiary hospital in Perth, Western Australia. Gestational cannabis and other substance use records were based on maternal self-report. Pregnancy outcomes included neonatal gestational age, birthweight, birth length, head circumference, resuscitation measures, special care nursery admission, 5-min Apgar score and initial neonatal feeding method. RESULTS: Among 3104 pregnant women (mean age: 31 years), gestational cannabis use was reported by 1.6% (n = 50). Cannabis users were younger, more likely to use other substances and experience mental illness or domestic violence compared with non-users. Neonates born to cannabis users had a lower mean gestational age, birthweight and birth length compared to those born to non-cannabis users. Gestational cannabis use (odds ratio (OR) 3.3, 95% confidence interval (CI) 1.6-6.7) and tobacco smoking (OR 2.2, 95% CI 1.5-3.6) were associated with increased odds of a low-birthweight neonate. Combined cannabis and tobacco use during pregnancy further increased the likelihood of low birthweight (LBW, adjusted OR 3.9, 95% CI 1.6-9.3). Multivariate logistic regression analysis adjusted for maternal sociodemographical characteristics, mental illness, alcohol, tobacco and other substance use demonstrated gestational cannabis use to be independently associated with LBW (OR 2.3, 95% CI 1.1-5.2). CONCLUSION: Gestational cannabis use was independently associated with low birthweight, synergistically affected by tobacco smoking.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".