Opioid use during pregnancy: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Opioid use has increased dramatically in North America. The safety of opioids in pregnancy is uncertain, but they are associated with several fetal abnormalities and contribute to rising rates of neonatal abstinence syndrome. We examined opioid use before and during pregnancy in a complete population-based cohort. METHODS: We examined opioid use in a cohort of all pregnant women in Manitoba, Canada, from 2001 to 2013. Opioid use was defined by prescriptions for opioids, converted to oral morphine equivalents (MEQ), during the 3 months before pregnancy and for each trimester. Given that the exposure per person may vary (because not all women complete all time periods), we determined a weighted number of pregnancies in each period. RESULTS: During the study period, 174 848 completed pregnancies were eligible for analysis (173 680 live births and 1168 stillbirths and intrauterine deaths), which represented a weighted value of 175 174 pregnancies. Among these pregnancies, 6.7% of the women filled opioid prescriptions in the 3 months before pregnancy. Use declined to 4.2% during the first trimester and further declined to 3.0% and 2.9% in the second and third trimesters, respectively. Over the study period, there was a modest increase in opioid use overall (from 7.3% to 7.7%). MEQ did not decline during pregnancy, and the mean MEQ increased significantly over the study period (from 284 mg to 1218 mg). Prescriptions for codeine were filled by 96.9% of the users, accounting for 66.2% of MEQ. INTERPRETATION: Although many of the women using opioids before pregnancy discontinued or reduced use of these drugs during pregnancy, the volume of opioids consumed by those who continued opioid use did not decline during pregnancy. The increasing dosage and increased use of higher-potency opioids by pregnant women highlights the need for continued evaluation of and education about the benefits and risks of this practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".