Mortality Following Childbirth in Ontario: A 20-Year Analysis of Temporal Trends and Causes
Bibliographic record
Abstract
OBJECTIVES: Maternal death during or after pregnancy is often preventable and accurate surveillance is key to prevention. We examined the number and causes of maternal death in Ontario over 20 years. METHODS: Retrospective cohort study including all hospital livebirths and stillbirths from 2002-2022 in the Canadian Institute for Health Information Discharge Abstracts (for hospitalizations) and National Ambulatory Care System (for emergency department encounters) linked to the Better Outcomes and Registry and Network births. Death was ascertained from childbirth to 365 days thereafter; all deaths were reviewed by at least 3 clinicians. RESULTS: There were 485 deaths among 2 764 214 live and stillbirths over 20 years-a maternal mortality ratio (MMR) of 17.5 per 100 000 (95% CI 16.0-19.2). There were 222 (45.8%) early deaths within 42 days of birth (MMR of 8.0 per 100 000; 95% CI 7.0-9.2), and 263 (54.2%) late deaths from 43 to 365 days after birth (MMR 9.5 per 100 000; 95% CI 8.4-10.7). Death was pregnancy-related in 169/485 cases (34.8%). Early death causes were predominantly hemorrhage, infection, preeclampsia, and pulmonary embolism. The top causes of 263 late deaths were cancer, injury, and cardiac arrest, or unknown. CONCLUSIONS: Most deaths within 1 year of childbirth are not related to obstetrical factors; however, pregnancy complications factor in early deaths. Causes of early and late deaths differ, but examining late deaths is equally important to identify factors not regularly examined in maternal mortality. As death in early pregnancy or outside hospitals is not reported, mortality is likely higher.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".