Screening for congenital heart defects by transabdominal ultrasound – role of early gestational screening and importance of operator training
Bibliographic record
Abstract
The majority of congenital heart defects occur without identifiable risk factors. Detection rates are therefore highly dependent on the experience and expertise of the obstetrical screening operator. In the first trimester, the risk of congenital heart defects increases with increasing nuchal thickness (≥2.5 mm detects 44% of major congenital heart defects), but because of the number of false positives, the positive predictive value is only a few percent. The anatomy of major congenital heart defects may be delineated in less than half of the fetuses during early second trimester. The reported yield of congenital heart defects detection during the mid-gestational routine obstetrical screening has improved over time and detection rates up to 85% of major congenital heart defects have been reported when outflow tract and three-vessel views are included in conjunction with the four-chamber view. Improved detection rates have been achieved following screening operator training interventions combined with a low referral threshold to obtain a detailed fetal echocardiographic study.
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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 | 0.005 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".