Abstract 17195: Accuracy of Fetal Echocardiography in the Current Era
Bibliographic record
Abstract
Accuracy of Fetal Echocardiography in Defining Anatomical Details of Fetal Cardiac Pathology in the Current Era Background: Enhanced ultrasound technology and improvements in prenatal detection have provided opportunities to fine-tune fetal cardiac diagnoses. We sought to determine the accuracy of fetal echocardiography in defining anatomical details of major structural fetal heart disease(FHD) Methods: We queried the fetal echo database at the University of Alberta to identify pregnancies with a major FHD diagnosis between 2007-2018. We excluded simple septal defects, minor valve abnormalities and isolated coarctation. FHD was divided into 12 major categories to examine differences within subtypes. Fetal echocardiography reports were compared to post-natal echocardiography or autopsy when available, to assess segmental anatomy. Differences were described according to the following categories:1) No difference between fetal and postnatal findings 2) Minor differences with no impact on outcome (left superior vena cava), 3) Minor differences that could make a minor difference to the delivery plan or surgery (e.g. vascular ring), 4) Major differences that could lead to a change in the course of pregnancy, delivery or surgical planning (e.g. ductal dependency); 5) Errors of categorization that did not alter surgical planning but could have changed counseling (e.g. associations with extra-cardiac pathology). Results: In the study period 744 pregnancies with major FHD were encountered of which 151 (20.3%) had pregnancy termination, 43(6%) intrauterine demise, 6(1%) were lost to followup. 524(71%) were liveborn. Of the 744, 542 (524 echo and 18 autopsy) had confirmation of full cardiac anatomy. Table 1 details the number of cases in whom differences were demonstrated. Conclusion: Fetal echocardiography in the current era is highly accurate with few serious errors. Delineating the anatomy of the outflow tracts in complex cardiac lesions remains a challenge
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".