Infertility treatment and risk of severe maternal morbidity: a propensity score–matched cohort study
Bibliographic record
Abstract
BACKGROUND: The extent to which infertility treatment predicts severe maternal morbidity is not well known. We examined the association between infertility treatment and severe maternal morbidity in pregnancy and the postpartum period. METHODS: We conducted a cohort study using population-based registries from Ontario between 2006 and 2012. Pregnancies achieved using infertility treatment (ovulation induction, intrauterine insemination or in vitro fertilization with or without intracytoplasmic sperm injection) were compared with unassisted pregnancies using propensity score matching, based on demographic, reproductive and obstetric factors. The primary outcome was a validated composite of severe maternal morbidity or maternal death from 20 weeks' gestation to 42 days postpartum. We also calculated the odds ratio of a woman having 1, 2, or 3 or more severe maternal morbidity indicators in relation to invasive (e.g., in vitro fertilization) or noninvasive (e.g., intrauterine insemination) infertility treatment. RESULTS: We matched 11 546 infertility treatment pregnancies with 47 553 untreated pregnancies. Severe maternal morbidity or maternal death occurred in 356 infertility-treated pregnancies (30.8 per 1000 deliveries) versus 1054 untreated pregnancies (22.2 per 1000 deliveries); relative risk 1.39 (95% confidence interval [CI] 1.23-1.56). The likelihood of a woman having 3 or more severe maternal morbidity indicators was increased in women who received invasive infertility treatment (odds ratio [OR] 2.28, 95% CI 1.56-3.33) but not in those who received noninvasive infertility treatment (OR 0.99, 95% CI 0.57-1.72). INTERPRETATION: Women who undergo infertility treatment, particularly in vitro fertilization, are at somewhat higher risk of severe maternal morbidity or death. Efforts are needed to identify patient- and treatment-specific predictors of severe maternal morbidity that may influence the type of treatment a woman is offered.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".