The effect of socioeconomic status on adverse obstetric and perinatal outcomes in women with polycystic ovary syndrome—An evaluation of a population database
Bibliographic record
Abstract
Abstract Objective To evaluate the modifying effect of low socioeconomic status (SES) on polycystic ovary syndrome (PCOS) women's pregnancy and neonatal complications. Methods A retrospective population‐based cohort study including all women with an ICD‐9 diagnosis of PCOS in the US between 2004 and 2014, who delivered in the third trimester or had a maternal death. SES was defined according to the total annual family income quartile for the entire population studied. We compared women in the lowest income quartile (<$39 000 annually) to those in the higher income quartiles combined (≥$39 000 annually). Pregnancy, delivery, and neonatal outcomes were compared between the two groups. Results Overall, 9 096 788 women delivered between 2004 and 2014, of which 12 322 had a PCOS diagnosis and evidence of SES classification. Of these, 2117 (17.2%) were in the lowest SES group, and 10 205 (82.8%) were in the higher SES group. PCOS patients in the lowest SES group, compared to the higher SES group, were more likely to be younger, obese (body mass index ≥30 kg/m2), to have smoked tobacco during pregnancy, and to have chronic hypertension and pregestational diabetes mellitus (DM) (P < 0.05). In a multivariate logistic regression, women in the lowest SES group, compared to the higher SES group, had increased odds of pregnancy‐induced hypertension (aOR 1.27, 95% CI: 1.12–1.46, P < 0.001), pre‐eclampsia (aOR 1.37, 95% CI: 1.14–1.65, P < 0.001), and cesarean delivery (aOR 1.21, 95% CI: 1.09–1.34, P < 0.001), with other comparable pregnancy, delivery and neonatal outcomes. Conclusion In PCOS patients, low SES increases the risk for pregnancy‐induced hypertension, pre‐eclampsia and CD, highlighting the importance of diligent pregnancy follow‐up and pre‐eclampsia prevention in these patients.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".