Role of Fetal Cardiac ECHO and Fetal Cardiac MRI in Prediction of Post Natal Hemodynamic Maladaptation in Fetuses of Diabetic Mothers
Bibliographic record
Abstract
Background: Maternal diabetes mellitus (DM) is defined as a degree of glucose intolerance with recognition during pregnancy (pregestational or gestational, type 1 or type 2), and it is estimated to be 15% in pregnant females in 2019. The current study aimed to study the role of fetal echocardiography and fetal cardiac magnetic resonance imaging (MRI) in anticipation and diagnosis of hemodynamic maladaptation changes in fetuses of diabetic mothers as predictors for (closure of Patent ductus arteriosus (PDA), persistent pulmonary hypertension and diastolic dysfunction) which were confirmed with post-natal echocardiographic and clinical assessment. Methods: This study was an observational follow-up study that included 80 pregnant diabetic females who were following in the specialized clinics of Ain Shams University Hospitals during the period starting from August 2020 till December 2022. Results: Diabetes affected left ventricle (LV) diastolic function and global cardiac function but did not affect LV systolic function (p-value=0.000). Interventricular septum (IVS) thickness had a significant positive correlation with LV- Myocardial performance index (MPI) and indices of DM such as HbA1c. Diastolic dysfunction was the most affected cardiac parameter in infants of diabetic mothers (p-value=0.002). The hemoglobin A1c (HbA1c) was the most significant predictor for IVS thickness in diabetic mothers' fetuses, especially with a long duration of DM (p-value=0.012). Conclusion: Fetal echocardiography in diabetic mothers is essential and the gold standard modality not only for diagnosing structural abnormalities but also for evaluating the cardiac function of the fetuses. Fetal cardiac MRI could be considered a complementary imaging modality that uses no ionizing radiation, good tissue contrast, and a larger field of view.
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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 | 0.000 |
| 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".