EP04.03: Diagnostic accuracy of fetal echocardiography in detecting coarctation of aorta: a systematic review and meta‐analysis
Bibliographic record
Abstract
The aim of this systematic review was to ascertain the diagnostic accuracy of different echocardiographic measures in detecting coarctation of aorta in the third trimester of pregnancy. Medline, Embase, Cinhal and Cochrane databases were searched. The following ultrasound signs were explored: vascular disproportion, Z scores tricuspid (TV) and mitral valves (MV), TV/MV ratio, Z scores pulmonary (PV) and aortic valves (AV), PV/AV ratio, Z-score aortic isthmus, Z score arterial duct, isthmus arteria duct ratio, presence of persistent left superior vena cava (PLSVC). Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale. The results were expressed as mean difference or relative risks (RR). Ten studies (8034 fetuses were included), 425 had coarctation of aorta. Fetuses with cardiac chambers and great vessels disproportion had a significantly higher risk of coarctation (RR: 1.03, 95% CI 1–1.1 and RR: 1.09, 95% CI 1.6-2.4 respectively). Fetuses with coarctation had a significantly higher MV Z score (−2.1 ± 1.5 vs 1.2 ± 1.1, p = 0.0001), AV Z score (2.3 ± 1.1 vs 0.84 ± 0.7, p = 0.0001) a smaller aortic valve diameter (4.2 ± 0.8 vs 5.7 ± 0.9, p = 0.0001), abnormal PV/AV ratio (RR: 2.6, 95% CI 2-.0-3.6) and higher Z score of the aortic isthmus (3,8 ± 1 vs 1.4 ± 0.9, p = 0.001). The results of this systematic review showed that detailed third trimester fetal echocardiography is able to detect coarctation in a significant proportion of fetuses. Further studies are needed to build reliable prediction models integrating different ultrasound signs in order to improve the antenatal detection of this anomaly.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".