In Vitro Fertilization and Adverse Pregnancy Outcomes in the Elective Single Embryo Transfer Era
Bibliographic record
Abstract
OBJECTIVE: Our objective was to estimate the association between in vitro fertilization (IVF) pregnancy and adverse pregnancy outcomes during delivery hospital admission in a contemporary, nation-wide cohort of births in the United States. STUDY DESIGN: This retrospective, population-based cohort study used the National Inpatient Sample database to identify patients discharged from the hospital following delivery from 2014 to 2019. IVF pregnancies were identified using the International Classification of Disease-Revision 9/10 codes. Crude and adjusted odds ratios of preterm birth and other clinically significant adverse pregnancy outcomes were evaluated using multivariable logistic regression models. Trends in preterm birth and multiple pregnancy were estimated over the study period. The contribution of multiple pregnancy to preterm birth in IVF pregnancy was estimated in a mediation analysis. RESULTS: -value for linear trend = 0.009). The proportion of multiple pregnancies decreased in IVF pregnancy delivery discharges but remained stable in non-IVF pregnancy deliveries. The proportion of the adjusted effect of IVF pregnancy on preterm birth mediated through multiple pregnancy was 67.6% (95% CI: 62.6-72.7%). CONCLUSION: While the odds of adverse pregnancy outcomes are increased compared with non-IVF pregnancies, the odds of preterm birth and multiple gestation have decreased among IVF pregnancies in the United States. KEY POINTS: · Pregnancies conceived by in vitro fertilization (IVF) are at significantly higher risk of multiple gestation, preterm birth, and other pregnancy complications.. · Recent guidelines for artificial reproductive treatments recommend single-embryo transfer in IVF.. · Using population-wide data, we demonstrate a significant gradual decline in the rates of preterm birth and other pregnancy complications following IVF in the last decade, mostly mediated by a reduction in multiple pregnancies..
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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 | 0.000 | 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 | 0.001 |
| 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".