Risk of recurrent severe maternal morbidity: a population-based study
Bibliographic record
Abstract
BACKGROUND: Severe maternal morbidity is a composite indicator of maternal health and obstetrical care. Little is known about the risk of recurrent severe maternal morbidity in a subsequent delivery. OBJECTIVE: This study aimed to estimate the risk of recurrent severe maternal morbidity in the next delivery after a complicated first delivery. STUDY DESIGN: We analyzed a population-based cohort study of women with at least 2 singleton hospital deliveries between 1989 and 2021 in Quebec, Canada. The exposure was severe maternal morbidity in the first hospital-recorded delivery. The study outcome was severe maternal morbidity at the second delivery. Log-binomial regression models adjusted for maternal and pregnancy characteristics were used to generate relative risks and 95% confidence intervals comparing women with and without severe maternal morbidity at first delivery. RESULTS: Among 819,375 women, 43,501 (3.2%) experienced severe maternal morbidity in the first delivery. The rate of severe maternal morbidity recurrence at second delivery was 65.2 vs 20.3 per 1000 in women with and without previous severe maternal morbidity (adjusted relative risk, 3.11; 95% confidence interval, 2.96-3.27). The adjusted relative risk for recurrence of severe maternal morbidity was greatest among women who had ≥3 different types of severe maternal morbidity at their first delivery, relative to those with none (adjusted relative risk, 5.50; 95% confidence interval, 4.26-7.10). Women with cardiac complication at first delivery had the highest risk of severe maternal morbidity in the next delivery. CONCLUSION: Women who experience severe maternal morbidity have a relatively high risk of recurrent morbidity in the subsequent pregnancy. In women with severe maternal morbidity, these study findings have implications for prepregnancy counseling and maternity care in the next pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".