Maternal and Neonatal Outcomes in Pregnancies With Adenomyosis [ID: 1375447]
Bibliographic record
Abstract
INTRODUCTION: Adenomyosis is a common gynecologic disease involving the uterus with effect on pregnancy being poorly understood. This study aimed to evaluate the associations between adenomyosis and obstetrical and newborn outcomes. METHODS: Using the Healthcare Cost and Utilization Project–National Inpatient Sample from the United States, we conducted a retrospective cohort study of all birth-related admissions from 2016 to 2019. Pregnancies with adenomyosis were identified using the ICD-10 code N80.0, with the remaining pregnancies being the reference group. Multivariate logistic regression models, adjusting for baseline maternal demographics, were used to determine the effect of adenomyosis on pregnancy outcomes. In light of the diagnosis bias inherent in births by cesarean delivery, adjustment for mode of delivery was performed on all outcome analyses. RESULTS: Among the 2,943,532 women who delivered between 2016 and 2019, 1,084 had adenomyosis, for an overall prevalence of 36 cases per 100,000 births, which was stable throughout the study period. Adenomyosis in pregnancy was associated with increased frequency of placenta abruptio (odds ratio 1.67, 95% CI 1.17–2.39), preterm delivery (1.36, 1.16–1.59), preterm premature rupture of membranes (1.31, 1.07–1.61), postpartum hemorrhage (2.65, 2.14–3.27), postpartum transfusion (2.20, 1.62–2.99), disseminated intravascular coagulation (9.31, 4.15–20.92), sepsis (2.67, 1.60–4.45), congenital anomalies (1.95, 1.34–2.84), and intrauterine fetal demise (1.98, 1.02–3.84). CONCLUSION: Adenomyosis in pregnancy is associated with adverse obstetric and fetal outcomes. Pregnancies in patients with adenomyosis should be considered at higher risk and may benefit from delivering in centers capable of managing postpartum hemorrhage.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".