How does the need for IVF affect pregnancy complications among multiple gestations? The study of a large American population database including almost 100,000 multiple gestations
Bibliographic record
Abstract
Objective: This study's aim is to compare pregnancy outcomes in multifetal gestations that were conceived spontaneously compared to in vitro fertilization (IVF). Few population-based studies have addressed this topic. Study design: This is a retrospective cohort study using the Health Care Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) database. Our study cohort included 90,552 multifetal gestations conceived spontaneously and 3219 IVF conceptions, from 2008 to 2014, inclusively. Multivariate logistic regression analyses were performed comparing maternal and neonatal outcomes, whilst adjusting for confounding variables. Subject was conducted using ICD-9 codes for multifetal gestation: 651. X and 76.1 and ICD-9 code for IVF: 23.85. Each pregnancy was included once. Results and conclusion: IVF multifetal gestations had increased risk of pregnancy-induced hypertension (aOR 1.31, 95 % CI 1.20-1.43), gestational hypertension (aOR 1.21, 95 % CI 1.04-1.41), preeclampsia (aOR 1.31, 95 % CI 1.19-1.45), gestational diabetes (aOR 1.26, 95 % CI 1.13-1.41) and placenta previa (aOR 1.7, 95 % CI 1.32-2.19). IVF delivery outcomes were more likely complicated by cesarean section (aOR 1.21, 95 % CI 1.10-1.33), preterm premature rupture of membranes (aOR 1.33, 95 % CI 1.16-1.52), chorioamnionitis (aOR 1.71, 95 % CI 1.37-2.14), postpartum hemorrhage (aOR 1.44, 95 % CI 1.26-1.63) and transfusions (aOR 1.48, 95 %CI 1.26-1.74). IVF neonatal outcomes were more likely complicated by small for gestational age (aOR 1.26, 95 % CI 1.12-1.41) and congenital anomalies (aOR 1.82, 95 % CI 1.29-2.57). IVF was not found to increase risks of eclampsia, preterm delivery, operative vaginal delivery, hysterectomy, or intrauterine fetal demise.IVF increased the risk of pregnancy, delivery, and neonatal outcomes in multifetal pregnancies with risks increased from 20 % to 70 %. The role of infertility versus the need for IVF and the type of IVF protocol used should be further evaluated.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".