Obstetrical and neonatal outcomes among pregnancies complicated by hyperparathyroidism
Bibliographic record
Abstract
PURPOSE: Severe hypercalcemia resulting from hyperparathyroidism may result in adverse perinatal outcomes. The objective of this study was to evaluate maternal and neonatal outcomes among pregnant women with hyperparathyroidism using a population database. METHODS: A retrospective cohort study was conducted using data from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample from 1999-2015. ICD-9 codes were used to identify women diagnosed with hyperparathyroidism during pregnancy. Perinatal outcomes between pregnant women with and without hyperparathyroidism were compared. Multivariate logistic regression, controlling for age, race, income, insurance type, hospital location, and comorbidities, evaluated the effect of hyperparathyroidism on perinatal outcomes. RESULTS: < 0.0001). Women with hyperparathyroidism were older and had more comorbidities, such as obesity, and pre-gestational hypertension and diabetes. Relative to the comparison group, women with hyperparathyroidism were more likely to deliver preterm, OR 1.69 (95% CI 1.24-2.29), to develop preeclampsia, 3.14 (2.30-4.28), and to deliver by cesarean, 1.69 (1.36-2.09). Infants born to mothers with hyperparathyroidism were more likely to be growth restricted, 1.83 (1.08-3.07), and to be diagnosed with a congenital anomaly, 4.21 (2.09-8.48). CONCLUSION: Hyperparathyroidism during pregnancy is associated with a significant increase in adverse perinatal outcomes, including preeclampsia, preterm delivery, fetal growth restriction, and congenital anomalies. As such, pregnancies among women with hyperparathyroidism should be considered high-risk, and specialized care is recommended in order to minimize maternal and neonatal morbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".