Use of prescription opioids and other psychotropic drugs during pregnancy and their impact on the mother and developing child: protocol for a cohort study using linked administrative data from Manitoba and British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Opioids are prescribed to manage pain. Approximately 1 in 20 pregnant women in Canada are prescribed opioids during the prenatal period, which may occur concurrently with other psychotropic drug use. The health implications of the independent and concurrent prenatal use of these drugs are not fully understood; however, adverse neonatal and longer-term outcomes have been suggested. This protocol describes a study to update the epidemiology of prenatal exposure to opioid and other psychotropic drug use during pregnancy, providing an enhanced understanding of the potential impacts on the mother and child to help inform decisions regarding prescription and use. METHODS AND ANALYSIS: The retrospective cohort study design uses population-based administrative data from Manitoba and British Columbia, Canada, to investigate the effect of prenatal opioid and concurrent psychotropic drug use on maternal and child outcomes. All mother-child dyads from 2000/2001 to 2019/2020 (approximately 1M pairs) will be identified and assigned to exposure groups based on the number of opioid and other psychotropic drug dispensations to the mother during the prenatal period. Maternal sociodemographic characteristics, prescribing patterns, short- and long-term child health and education outcomes and maternal outcomes will be examined. ETHICS AND DISSEMINATION: The study was approved by the University of Manitoba Human Research Ethics Board (No. HS24397 - H2020:470) and the University of British Columbia Clinical Research Ethics Board (No. H21-02262). The study will generate findings that will add to the growing body of evidence of potential short- and long-term adverse effects on children exposed to these drugs prenatally and will help to inform safe prescribing guidelines during pregnancy. Results will be published in peer-reviewed journals.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".