Canadian perinatal opioid project: a protocol for a national health data system
Bibliographic record
Abstract
INTRODUCTION: Opioids are widely used during pregnancy and can lead to health complications for pregnant people, parents and their children. Yet, little is known about the long- and short-term effects of perinatal opioid exposures on health outcomes in Canada. Evidence is needed to inform optimal support for maternal and child health following perinatal opioid exposures. AIM: We aim to develop the Canadian Perinatal Opioid Project, a pan-Canadian federated health data system to capture perinatal opioid exposures across multiple provinces, along with subsequent maternal and child health outcomes. METHODS AND ANALYSIS: This health data system uses population-based administrative health records from Alberta, British Columbia, Manitoba, Ontario and Saskatchewan, each with two population-based cohorts: (1) all pregnancies among people aged 12-49 years, 2013-2023 and (2) liveborn infants from these pregnancies. Pregnant people will be followed for 1 year after the end of pregnancy; live births will be followed for 8 years. Data will be obtained from outpatient prescription opioid records, mother-infant linked hospitalisation records, emergency department visits, outpatient physician visits, birth registries and vital statistics. This work is being conducted in collaboration with Indigenous and non-Indigenous people with lived/living experience of perinatal opioid use and knowledge users. ANALYSIS: We will use descriptive statistics to describe incidence and cohort characteristics and Poisson regression to assess annual trends. Patient-level analysis will occur in each province, and province-level aggregated results will be meta-analysed. ETHICS AND DISSEMINATION: Ethics approval was granted by research ethics boards at the University of British Columbia (H24-03406), University of Calgary (REB24-1721), University of Manitoba (HS26640), University of Toronto (46764) and University of Saskatchewan (5348). We will develop knowledge dissemination plans and products with people with lived/living experience of perinatal opioid use and knowledge users. Health data indicators will be featured in an open-access online dashboard. We expect to share methods and research findings through peer-reviewed publications in high-impact journals, presentations at national and international conferences, presentations to community members and knowledge users, and research summaries for the general population.
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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 | 0.103 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.110 | 0.018 |
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".