Increasing the Detection Rate of Normal Fetal Cardiac Structures: A Real-Time Approach
Bibliographic record
Abstract
The purpose of this study was to determine if acquiring real-time sweeps of the fetal heart would be a more effective method of identifying normal cardiac structures compared with using static images during routine second-trimester obstetric sonograms. Subjects were scanned using three different techniques. The static image acquisition (protocol A) included three images of the fetal heart. Protocol B used two gray-scale sweeps through the fetal heart. Protocol C acquired three color loops of the fetal heart. The sweeps demonstrated a complete normal cardiac assessment in 71% of studies, compared with the static image and color Doppler techniques that completed a normal cardiac assessment in only 39% of studies, respectively. The real-time technique detected four chambers, the left ventricular outflow tract (LVOT), the right ventricular outflow tract (RVOT), the LVOT/RVOT crossover, and size and axis of the heart with a greater frequency than the static images and color loops in all cases. In addition, the real-time technique was able to demonstrate the pulmonary veins in 56% of cases compared with 3.6% for static images. The color Doppler acquisition demonstrated blood flow through the atrial-ventricular and semilunar valves in 86% of cases.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".