Performance of comprehensive first trimester fetal anatomy assessment
Bibliographic record
Abstract
OBJECTIVE: Ultrasound assessment of the fetal anatomy and fetal echocardiography are feasible in the first trimester of pregnancy. This study was designed to assess the performance of a comprehensive fetal anatomy assessment in a high-risk population at a tertiary fetal medicine unit. METHODS: A retrospective review of high-risk patients undergoing comprehensive fetal anatomy ultrasound assessment between 11 weeks and 13 + 6 weeks of gestation was conducted. Findings of the early anatomy ultrasound scan were compared with those of the second trimester anatomy scan, and birth outcomes or post-mortem results. RESULTS: Early anatomy ultrasounds were performed in 765 patients. The sensitivity of the scan for detecting fetal anomalies compared to the birth outcome was 80.5% (95% CI 73.5-86.3) and specificity was 93.1% (95%CI 90.6-95.2). Positive and negative predictive values were 78.5% (95% CI 71.4-84.6) and 93.9% (95% CI 91.4-95.8), respectively. The most missed and overdiagnosed abnormalities were ventricular septal defects. The second trimester ultrasound had sensitivity of 69.0% (95% CI 55.5-80.5) and specificity of 87.5% (95% CI 84.3-90.2). CONCLUSIONS: In a high-risk population, early assessments had similar performance metrics as the second trimester anatomy ultrasound. We advocate for a comprehensive fetal assessment in the care of high-risk pregnancies.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".