A Comparison of Pregnancy and Neonatal Outcomes in Women with the Hyperandrogenic Disorders Polycystic Ovary syndrome and Cushing’s Syndrome
Bibliographic record
Abstract
Research Question: How does the risk for adverse obstetric outcomes differ among women with polycystic ovary syndrome (PCOS) and women with Cushing’s syndrome (CUS)? Design: A retrospective population-based study utilizing data from the Healthcare Cost and Utilization Project—Nationwide Inpatient Sample (HCUP-NIS), 2004-2014. 14, 881 deliveries to women with PCOS and 134 deliveries to women with CUS were identified. Associations between PCOS, CUS, pregnancy, delivery, and neonatal outcomes were analyzed with multivariate logistic regression analysis. Results: At baseline, CUS was associated with a higher risk of chronic hypertension (P<0.001), pregestational diabetes mellitus (P=0.01), thyroid disease (P=0.004), and higher rates of smoking during pregnancy (P=0.02) whereas PCOS was associated with higher rates of obesity (P=0.01). In terms of obstetric outcomes, PCOS increased the prevalence of gestational diabetes mellitus (P=0.002, adjusted[a] OR 2.73; 95% CI 1.46 to 5.12), and cesarean section (P<0.001, aOR 2.63; 95% CI 1.81-3.83) in comparison to CUS. CUS increased the prevalence of operative vaginal delivery (P<0.001, aOR 0.10; 95% CI 0.06-0.14), and transfusion (P=0.002, aOR 0.25; 95% CI 0.11-0.59) in comparison to deliveries to women with PCOS. No significant differences were found in terms of pregnancy-induced hypertension (P=0.78), gestational hypertension (P=0.86), preeclampsia (P=0.25), preeclampsia or eclampsia superimposed on pre-existing hypertension (P=0.13). Conclusion: PCOS increases the risk of gestational diabetes and cesarean section relative to CUS, whereas CUS increases the prevalence of operative vaginal delivery and blood transfusions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".