Severe Maternal Morbidity and Infant Mortality in Canada
Bibliographic record
Abstract
BACKGROUND: Severe maternal morbidity (SMM) comprises an array of conditions and procedures denoting an acutely life-threatening pregnancy-related condition. SMM may further compromise fetal well-being. Empirical data are lacking about the relation between SMM and infant mortality. METHODS: This population-based cohort study included 1 892 857 singleton births between 2002 and 2017 in Ontario, Canada, within a universal health care system. The exposure was SMM as an overall construct arising from 23 weeks' gestation up to 42 days after the index delivery. The primary outcome was infant mortality from birth to 365 days. Multivariable modified Poisson regression generated relative risks and 95% confidence intervals (CIs), adjusted for maternal age, income, rurality, world region of origin, diabetes mellitus, and chronic hypertension. RESULTS: Infant mortality occurred among 174 of 19 587 live births with SMM (8.9 per 1000) vs 5289 of 1 865 791 live births without SMM (2.8 per 1000) (an adjusted relative risk of 2.93 [95% CI 2.51-3.41]). Of 19 587 pregnancies with SMM, 4523 (23.1%) had sepsis. Relative to births without SMM, the adjusted odds ratio for infant death from sepsis was 1.95 (95% CI 1.10-3.45) if SMM occurred without maternal sepsis and 6.36 (95% CI 3.50-11.55) if SMM included sepsis. CONCLUSIONS: SMM confers a higher risk of infant death. There is also coupling tendency (concurrent event of interest) between SMM with sepsis and infant death from sepsis. Identification of preventable SMM indicators, as well as the development of strategies to limit their onset or progression, may reduce infant mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".