Effect of hidradenitis suppurativa on obstetric and neonatal outcomes
Bibliographic record
Abstract
PURPOSE: Hidradenitis suppurativa (HS) is a debilitating chronic inflammatory skin disease with an often-unsatisfactory response to treatment. The objective was to evaluate the association between HS and pregnancy, delivery and neonatal outcomes. METHODS: The United States' Healthcare Cost and Utilization Project-Nationwide Inpatient Sample database was used to conduct a retrospective cohort study among all women who delivered between 1999 and 2015. ICD-9 code 705.83 identified those with HS, with the remaining deliveries composing the comparison group. Multivariate logistic regression compared maternal and neonatal outcomes between these two groups, while adjusting for baseline maternal variables. RESULTS: The study included 13,792,544 deliveries, of which 1021 were associated with an HS diagnosis (7.4/100,000 deliveries). During the observation period, there was an upward trend in the prevalence of HS among pregnant women (<0.0001). Pregnant women with HS were more likely to be African-American, to belong to a lower income quartile, and to be insured by Medicaid. They were also more likely to smoke, to be morbidly obese, and to be hypertensive. Compared with women without HS, those with HS had a greater likelihood of developing preeclampsia (OR 1.36, 95% CI 1.08-1.71), delivering by cesarean section (OR 1.78, 95% CI 1.56-2.02), and having a baby with congenital anomalies (OR 2.00, 95% CI 1.10-3.62). CONCLUSIONS: Although HS is a complex skin disorder, pregnancies complicated by HS had comparable outcomes to non-HS pregnancies, with the exception of a greater risk of preeclampsia, cesarean sections, and congenital anomalies. Health-care providers and women should be aware of these HS associated risks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".