The Value of Detailed First-Trimester Ultrasound Anomaly Scan for the Detection of Chromosomal Abnormalities
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Bibliographic record
Abstract
PURPOSE: To evaluate the performance of first-trimester ultrasound screening involving a detailed anomaly scan for the detection of trisomy 18, trisomy 13, triploidy, Turner syndrome and trisomy 21. METHODS: Data of pregnant women who underwent aneuploidy screening at 11-13 weeks of gestation was retrospectively analyzed. Crown-rump length (CRL), fetal nuchal translucency thickness (NT) and nasal bone (NB) anatomy, blood flow across the tricuspid valve (TV) and through the ductus venosus (DV) were assessed. Furthermore, a detailed scan for fetal anatomical anomalies (FA) was carried out. Performance of these markers was assessed by logistic regression and ROC analyses for different screening models. RESULTS: 4005 fetuses were analyzed. 3856 were euploid, 149 aneuploid (trisomy 18: 40; trisomy 13: 14; triploidy: 3; Turner syndrome: 17; trisomy 21: 75 cases). 70-100 % of the fetuses with trisomy 18 and 13, triploidy and Turner syndrome but only 34.7 % with trisomy 21 had at least one fetal defect. Considering all aneuploidies, the detection rate (DR) for screening based on MA+NT+NB+TV+DV was 90.6 % and improved to 96.0 % if an FA was added (fixed false-positive rate: 3 %). If screening was based on MA+NT+FA, the detection rate for all aneuploidies was 85.2 %. However, the DR for trisomy 18, trisomy 13, triploidy and Turner syndrome (excluding trisomy 21) was 94.6 %, indicating the high diagnostic value of an anomaly scan for these aneuploidies. CONCLUSION: Incorporation of a detailed fetal anomaly scan (FA) into first-trimester screening algorithms can improve the detection rates for trisomy 18 and 13, triploidy and Turner syndrome.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it