Severe Maternal Morbidity and Maternal Mortality Associated with Assisted Reproductive Technology
Bibliographic record
Abstract
OBJECTIVE: To assess the association between use of assisted reproductive technologies (ART) and severe maternal morbidity and maternal mortality (SMM). METHODS: We carried out a cohort study that included all hospital deliveries at ≥20 weeks gestation in Canada (excluding Québec) between April 2009 and March 2018. Outcomes of interest included composite SMM and SMM types (e.g., severe preeclampsia, HELLP syndrome, and eclampsia; severe hemorrhage; acute renal failure). Multivariable regression was used to estimate crude and adjusted rate ratios (RR and aRR) and 95% confidence intervals (CI). RESULTS: The study included 2 535 056 women, of whom 72 023 (2.8%) delivered following the use of ART. The composite SMM rate for women who used ART was 34.7 per 1000 deliveries (95% CI 33.0-36.0) versus 11.5 per 1000 deliveries (95% CI 11.4-11.6) for women who did not use ART (RR 3.01; 95% CI 2.89-3.14). ART use was associated with SMM types such as severe preeclampsia, HELLP syndrome, and eclampsia (RR 3.50; 95% CI 3.27-3.73), severe hemorrhage (RR 3.58, 95% CI 3.27-3.92), and acute renal failure (RR 6.79; 95% CI 5.78-7.98). Associations between ART and composite SMM were attenuated but remained elevated after adjusting for maternal characteristics (aRR 2.34; 95% CI 2.24-2.45). Women who used ART and had a multi-fetal pregnancy had a 4.7 times higher rate of composite SMM compared with women who did not use ART and delivered singletons. CONCLUSION: Women who deliver following the use of ART have increased risks of SMM and require counselling that includes mention of the lower risks of SMM associated with ART-conceived singleton pregnancy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".