Gout in pregnancy: Obstetric and neonatal outcomes
Bibliographic record
Abstract
OBJECTIVE: The pregnancy, delivery, and neonatal outcomes of pregnancies complicated by gout have yet to be evaluated in a population-based study. We sought to evaluate the obstetric and neonatal outcomes in pregnant patients with gout using a national population database. METHODS: This is a retrospective population-based cohort study utilizing the Healthcare Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS). All women who delivered or had a maternal death in the USA (2004-2014) were included in the study. Pregnancy, delivery, and neonatal outcomes were compared between women with an ICD-9 diagnosis of gout to those without. RESULTS: Overall, 9 096 788 women met the inclusion criteria. Of these, 168 women (1.8/100000) had gout. Patients with gout, compared to those without, were more likely to be older and obese and to have chronic hypertension, pregestational diabetes mellitus, and thyroid disease. Pregnant women with gout were more likely to develop gestational diabetes mellitus (aOR 1.78 [95% CI 1.17-2.72], P = 0.008), to require operative vaginal delivery (aOR 3.26 [95% CI 2.03-5.22], P = 0.0001), and to experience venous thromboembolism (aOR 8.47 [95% CI 2.06-34.82], P = 0.003) compared to pregnant patients without gout. Patients with gout were more likely to deliver a neonate with congenital anomalies compared to those without gout (aOR 3.38 [95% CI 1.24-9.20], P = 0.02). CONCLUSION: Gout in pregnancy, or pregnancies complicated by a history of gout, are associated with increased risk of gestational diabetes mellitus, pulmonary embolism, and neonatal anomalies.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".