EP12.10: Cardiac disease in pregnancy and perinatal outcomes
Bibliographic record
Abstract
This is a retrospective cohort study. All pregnant women with diagnosed cardiac disease and gave birth at Prince Sultan Military Medical Center between January 2017 and July 2022 were included in the study. Data including maternal characteristics and perinatal outcomes were collected from the digital medical file database. Data has been analysed by using descriptive statistics via IBM SPSS® version 20. Continuous variables were expressed as mean (minimum – maximum) for non-parametric variables. Categorical variables were expressed as frequencies (n) and percentage of occurrence (%). Total of 116 pregnant women with cardiac disease were included in the study. All except one case were booked and had regular prenatal care. Majority among them were booked multiparous women, at age above 35 and, non-smoker. 51.2%, 15.1%, 14.6% and 2.5% were women with valvular, rheumatic, congenital heart disease and cardiomyopathies respectively. 6.7% developed hypertensive spectrum disorder and 10% developed gestational diabetes mellitus. 7 patients required intensive care unit admission but there were no maternal deaths. 12.6% newborns required NICU admission and 10% needed respiratory support. There were 4 (3.4%) fetal and 1 (.8%) neonatal death. In this cohort, incidence of Caesarean section and congenital heart defect among newborns were higher than reported in general population. Other perinatal outcomes were similar to the general population. Pregnant women with cardiac disease require optimal counselling and multidisciplinary team management throughout their pregnancies and postpartum period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".