Sickle cell disease and pregnancy outcomes: population-based study on 8.8 million births
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of sickle cell disease (SCD) in pregnancy, and to measure risk factors, morbidity, and mortality among women with SCD with and without crisis at the time of birth. METHODS: We conducted a population-based, retrospective cohort study on all births in the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP-NIS) from 1999 to 2008. Births to SCD with and without crisis were identified using ICD-9 codes. Adjusted effects of risk factors and outcomes were estimated using logistic regression analyses. Effect of hemoglobin variants among women with SCD was analyzed as a predictor of crisis. RESULTS: There were 4262 births to women with SCD for an overall prevalence of 4.83 per 10,000 deliveries. 28.5% of women with SCD developed crisis at the time of delivery. The maternal mortality rate was 1.6 per 1000 deliveries in women with SCD, compared to 0.1 per 1000 in women without SCD. Pregnant women with SCD had a higher risk of developing preeclampsia, eclampsia, venous thromboembolism, cardiomyopathy, intrauterine fetal demise, and intrauterine growth restriction. Cesarean delivery rates were higher in women with SCD. Among the 1898 SCD women with identified hemoglobin variants, homozygous SS was the greatest risk factor for sickle cell crisis, accounting for 89.8% of all women who developed crisis. CONCLUSION: Pregnant women with SCD have a high risk of morbidity and mortality. Developing acute sickle cell crisis worsened perinatal outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".