Pregnancy, delivery, and neonatal outcomes among women with beta-thalassemia major: a population-based study of a large US database
Bibliographic record
Abstract
PURPOSE: We explored the effect of beta-thalassemia major on pregnancy and delivery outcomes in non-endemic area, utilizing USA population database. METHODS: This is a retrospective study utilizing data from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample. A cohort of all deliveries between 2011 and 2014 was created using ICD-9 codes. The patients with beta-thalassemia major were identified and matched to patients without beta-thalassemia based on age, race, income quartile, and type of health insurance at a ratio of 1:20. The baseline characteristics were compared between the groups using Chi-square and Fischer's exact tests, as appropriate. The univariate and multivariate analyses were conducted for pregnancy, delivery and neonatal outcomes to estimate the unadjusted and adjusted odds ratio, respectively. RESULTS: Out of 3,070,656 pregnancies over the study period, beta-thalassemia major complicated 445 pregnancies. The patients with beta-thalassemia were more likely to have thyroid disorders and previous C-section (p-value < 0.05). There were no differences in pregnancy outcomes such as gestational hypertension, preeclampsia, gestational diabetes, and placenta previa. C-section was 30% more likely to be the method of birth (aOR 1.30, 95%CI 1.03-1.63) and there was more than three-fold increase in rate of blood transfusion (aOR 4.69, 95% CI 3.02-7.28) among participants with beta-thalassemia major. Mothers with beta-thalassemia, almost, were 70% more likely to have a neonate small for gestational age (aOR 1.68, 95%CI 1.07-2.62). CONCLUSIONS: Women with beta-thalassemia major are more likely to give birth by C-section, require blood transfusion and have small for gestational age neonates. Counseling patients with beta-thalassemia about these risks and increased antenatal surveillance is advised.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".