Polycystic Kidney Disease in Pregnancy and Obstetric Outcomes [ID 1340]
Bibliographic record
Abstract
INTRODUCTION: Polycystic kidney disease (PKD) is a rare genetic condition. The purpose of our study is to evaluate the effect of PKD on obstetric outcomes. METHODS: A retrospective cohort study was performed using the population-based American database National Inpatient Sample. We identified all births between the years 2016 and 2021. The exposure group was defined as PKD, and the comparison group was all other births. Baseline characteristics were described and multivariable logistic regression, adjusted for maternal age, was used to assess associations between PKD and obstetric maternal and fetal outcomes. RESULTS: We identified 1,139 patients with PKD and 4,336,473 patients without PKD who gave birth between the years 2016 and 2021. The overall incidence of PKD was 26 per 100,000 births, with no significant increase over the study period (P=.07). Compared with non-PKD patients, those with PKD were more often Caucasian, obese, and had preexisting hypertension. Polycystic kidney disease patients were more likely to deliver via cesarean (odds ratio 1.4 [95% CI, 1.2–1.5]) and were at greater risk of developing preeclampsia (2.4 [2.0–2.9]) and gestational diabetes (1.6 [1.3–1.9]). Polycystic kidney disease patients were also more likely to suffer from sepsis (3.5 [2.3–5.3]) and death (10.2 [2.5–40.7]). Neonates born from PKD patients were more likely to suffer from preterm birth (2.7 [2.4–3.1]), intrauterine growth restriction (1.8 [1.4–2.3]), and intrauterine fetal demise (1.7 [1.02–2.8]). CONCLUSIONS/IMPLICATIONS: Polycystic kidney disease patients and their fetuses/newborns are at a greater risk for obstetric complications and should be considered high-risk patients. As such, their pregnancies should be followed closely by obstetricians, nephrologists, and neonatologists.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".