Patterns of antiemetic medication use during pregnancy: A multi-country retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To compare patterns in use of different antiemetics during pregnancy in Canada, the United Kingdom, and the United States, between 2002 and 2014. METHODS: We constructed population-based cohorts of pregnant women using administrative healthcare data from five Canadian provinces (Alberta, British Columbia, Manitoba, Ontario, and Saskatchewan), the Clinical Practice Research Datalink from the United Kingdom, and the IBM MarketScan Research Databases from the United States. We included pregnancies ending in live births, stillbirth, spontaneous abortion, or induced abortion. We determined maternal use of antiemetics from pharmacy claims in Canada and the United States and from prescriptions in the United Kingdom. RESULTS: The most common outcome of 3 848 734 included pregnancies (started 2002-2014) was live birth (66.7% of all pregnancies) followed by spontaneous abortion (20.2%). Use of antiemetics during pregnancy increased over time in all three countries. Canada had the highest prevalence of use of prescription antiemetics during pregnancy (17.7% of pregnancies overall, 13.2% of pregnancies in 2002, and 18.9% in 2014), followed by the United States (14.0% overall, 8.9% in 2007, and 18.1% in 2014), and the United Kingdom (5.0% overall, 4.2% in 2002, and 6.5% in 2014). Besides use of antiemetic drugs being considerably lower in the United Kingdom, the increase in its use over time was more modest. The most commonly used antiemetic was combination doxylamine/pyridoxine in Canada (95.2% of pregnancies treated with antiemetics), ondansetron in the United States (72.2%), and prochlorperazine in the United Kingdom (63.5%). CONCLUSIONS: In this large cohort study, we observed an overall increase in antiemetic use during pregnancy, and patterns of use varied across jurisdictions. Continued monitoring of antiemetic use and further research are warranted to better understand the reasons for differences in use of these medications and to assess their benefit-risk profile in this 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".