Cannabis Use in Pregnancy and Downstream effects on maternal and infant health (CUPiD): a protocol for a birth cohort pilot study
Bibliographic record
Abstract
INTRODUCTION: Cannabis use in pregnancy and post partum is increasing. Accessibility to cannabis has expanded due to the legalisation of cannabis in Canada. Therefore, there is a critical need to monitor the impact of cannabis on pregnancy outcomes and infant neurodevelopment. This pilot study will assess the feasibility of modern recruitment and data collection strategies adapted to the current cannabis environment and inform the design of a multicentre prospective birth cohort. METHODS AND ANALYSIS: We will establish a pregnancy and birth cohort of 50 cannabis users and 50 non-users recruited before delivery. We will follow the participants at regular visits from recruitment to 12 weeks post partum. Participants will provide demographic and socioeconomic data, report their cannabis use patterns, and provide biological samples. Biological samples include maternal and infant urine and blood, breastmilk/chestmilk, cord blood, cord tissue, placenta and meconium. All samples will be processed and stored at -80°C until analysis by immunoassay or liquid chromatography-tandem mass spectrometry to determine the presence of cannabis metabolites. In addition, partners will be invited to provide additional socioeconomic and substance use data. ETHICS AND DISSEMINATION: Ethics was obtained from Ottawa Health Science Network Research Ethics Board through Clinical Trials Ontario (3791). Our findings will be published in peer-reviewed journals, presented at scientific conferences and shared broadly with patients, healthcare decision-makers, and project partners online and through social media. TRIAL REGISTRATION NUMBER: NCT05309226.Cite Now.
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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 | 0.037 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.013 |
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".