Acute poisoning in pregnancy: a province-wide perspective from a poison center
Bibliographic record
Abstract
Background: Poisoning in pregnancy can cause maternal and neonatal morbidity and mortality, but few data detail such events. Herein, we describe poisoning exposures in pregnant women identified by a large Canadian Poison Centre.Methods: This retrospective study evaluated poisoning exposures in pregnant women aged 12–60 years, reported to the Ontario Poison Centre from 2010 to 2017. Exposures were identified from the Poison Centre database by calls received, in which the patient was also reported to be pregnant. We collected patient demographics (age, trimester, and location), as well as information about the poisoning exposure (number and type of substances, route of exposure, reason for exposure, decontamination, and treatment recommendations).Results: There were 1716 cases of poisoning exposures during pregnancy over the eight-year study period, representing 0.28% of all 619,539 calls over the period. Median maternal age was 29 years (IQR 25–33), and exposures were most frequent in the second trimester of pregnancy (41%). Unintentional exposures (n = 1397) accounted for 81% of all calls. Of the 18% of calls (n = 305) for intentional exposures, 71% (n = 219) were suspected attempted suicides. Intentional exposures were more frequent in the first (OR 2.64, 95% CI 1.85–3.76) and second trimesters (OR 1.61, 95% CI 1.13–2.28), relative to third trimester. The associated risk of intentional exposures was more likely in women aged ≤19 years (OR 21.41, 95% CI 12.75–35.94) and 20–29 years (OR 3.72, 95% CI 2.70–5.14), relative to women ≥30 years old.Conclusions: Intentional poisoning exposures in pregnancy most commonly involve young women in the first two trimesters. Population-based studies are needed to further examine risk factors for overdose, poisoning, and self-harm in pregnancy, as well as perinatal outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".