Early anatomy ultrasound in women at increased risk of fetal anomalies
Bibliographic record
Abstract
OBJECTIVE: This study was designed to assess the accuracy of ultrasound anatomy screening before 17 weeks gestation in a population at high risk of fetal anomalies. METHODS: Retrospective review of anatomy ultrasound examinations carried out between 12-17 weeks gestation in a high-risk population. Early sonographic findings were compared with the 18-22 week anatomy ultrasound, karyotype, echocardiogram and postnatal/postmortem results. RESULTS: A complete anatomical survey was achieved in 68 of 101 screened fetuses (67%), with cardiac anatomy having the lowest completion rate (78/101; 77%). Anomalies were suspected on ultrasound in 23 fetuses. Four of these did not undergo pathologic examination but had clearly abnormal findings on ultrasound. Eighteen fetuses had a confirmed abnormal outcome. Sensitivity of early anatomy ultrasound was 83.3% (n = 15/18) and specificity 94.9% (n = 75/79). There were 3 false negative ultrasounds (16.6%: trisomy 21 with short humerus, choanal atresia and ventriculomegaly, and a ventricular septal defect). False positive rate was 4.0% (4 ventricular septal defects). CONCLUSION: The high rate of visualization of anatomic structures between 12-17 weeks gestation allows for either early detection of fetal anomalies or parental reassurance in many cases. Subtle anomalies of the heart remain difficult to diagnose.
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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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".