Risk Factors for Hyperemesis Gravidarum Requiring Hospital Admission During Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for hyperemesis requiring hospital admission during pregnancy. METHODS: Data from a population-based cohort of all deliveries in Nova Scotia, Canada between 1988 and 2002 were obtained from the Nova Scotia Atlee Perinatal Database. Women with 1 or more antepartum admissions for hyperemesis were compared with women with no admissions for hyperemesis. Relative risks (RRs) and 95% confidence intervals (CIs) were estimated using logistic regression and used to determine a set of independent risk factors for hyperemesis. RESULTS: The overall rate of admission for hyperemesis was 0.8% (n = 1,301) among 157,922 deliveries. In the adjusted analysis, hyperthyroid disorders (RR 4.5, 95% CI 1.8-11.1), psychiatric illness (RR 4.1, 95% CI 3.0-5.7), previous molar pregnancy (RR 3.3, 95% CI 1.6-6.8), preexisting diabetes (RR 2.6, 95% CI 1.5-4.7), gastrointestinal disorders (RR 2.5, 95% CI 1.8-3.6), and asthma (RR 1.5, 95% CI 1.2-1.9) were all statistically significant risk factors for hyperemesis, whereas maternal smoking and maternal age older than 30 were associated with decreased risk. Compared with singleton male pregnancies, singleton female pregnancies, pregnancies with multiple male fetuses, and male and female combinations were associated with statistically significant increased risk of hyperemesis. CONCLUSION: Although hospitalization for hyperemesis occurs in less than 1% of pregnant women, this translates to a large number of hospital admissions. The factors associated with hyperemesis are primarily medical and fetal factors that are not easily modifiable, but identification of these factors may be useful in determining those women at high risk for developing hyperemesis. LEVEL OF EVIDENCE: II-2.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".