Pregnancy, delivery and neonatal outcomes among women with hypoparathyroidism—A population‐based study
Bibliographic record
Abstract
OBJECTIVE: Data are inconclusive regarding pregnancy complications associated with maternal chronic hypoparathyroidism. Therefore, we aimed to compare pregnancy, delivery and neonatal outcomes in patients affected by chronic hypoparathyroidism to those without chronic hypoparathyroidism. DESIGN: A retrospective population-based study utilising data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP-NIS) database over 11 years from 2004 to 2014 inclusively. Multivariate logistic regression was used to control for confounders. PATIENTS: Patients with chronic hypoparathyroidism compared with those without. MEASUREMENTS: Obstetric and neonatal outcomes. RESULTS: We identified 204 pregnancies in mothers with chronic hypoparathyroidism and 9,096,584 pregnancies without chronic hypoparathyroidism. After adjusting for age, insurance plan type, obesity, chronic hypertension, thyroid disease, pregestational diabetes mellitus, and previous caesarean section, patients in the hypoparathyroidism group, compared with those without hypoparathyroidism, were found to have an increased rate of preterm birth (<37 weeks) (19.1% vs. 7.2%, aOR: 2.49, 95% confidence interval [CI]: 1.74-3.54, p < 0.0001, respectively); and blood transfusions (4.9% vs. 1.0%, aOR: 4.07, 95% CI: 2.15-7.73, p < -0.0001). Neonates to mothers with chronic hypoparathyroidism had a higher rate of congenital anomalies (4.4% vs. 0.4%, aOR: 6.50, 95% CI: 3.31-12.75, p < 0.0001), with comparable rates of small-for-gestational-age neonates and intrauterine foetal death. CONCLUSION: This is the largest study of chronic hypoparathyroidism in pregnancy to date. We found significant increases in the rates of preterm birth, blood transfusions and congenital anomalies in chronic hypoparathyroidism. Our findings highlight the importance of identifying chronic hypoparathyroidism as a risk factor for pregnancy and neonatal complications, although it remains unknown if maintaining calcium in the target range will mitigate these 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".