The Mathematical Limitations of Fetal Echocardiography as a Screening Tool in the Setting of a Normal Second-Trimester Ultrasound
Bibliographic record
Abstract
BACKGROUND: The effectiveness of screening strategies targeting pregnancies at higher risk of congenital heart disease (CHD) is reduced by the low prevalence of severe CHD, the increase in CHD detection rates by second-trimester ultrasound (U/S), and the high proportion of severe CHD in low-risk pregnancies. We aimed to determine situations in which additional screening by fetal echocardiography (FE) would result in a significant increase in sensitivity and a sizable decrease in the false-negative rate of detection of severe CHD. METHODS: We simulated the change in the numbers of detected severe CHD cases when FE is offered to women with a normal second-trimester U/S who have a higher risk of bearing a child with CHD, compared to U/S alone. The primary outcome was the increase in sensitivity. Secondary outcomes were the number needed to screen and the reduction in the rate of missed cases. RESULTS: For an U/S sensitivity of 60%, the addition of FE in pregnancies at high risk of CHD (risk ratio 3.5; range: 2 to 5) increased sensitivity by 2.4 percentage points (1.1 to 7.9). The number needed to screen to detect one additional case of severe CHD was 436 (156 to 952). The rate of additional severe CHD cases detected by FE was 4 per 100,000 pregnancies (2 to 32). CONCLUSIONS: The addition of FE to U/S for severe CHD prenatal screening in pregnancies at high risk of CHD yielded marginal benefits in terms of increased sensitivity and decreased rates of false negatives, at the expense of significant resource utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".