Sonographic examination of the fetal central nervous system: guidelines for performing the ‘basic examination’ and the ‘fetal neurosonogram’
Bibliographic record
Abstract
Central nervous system (CNS) malformations are some of the most common of all congenital abnormalities. Neural tube defects are themost frequent CNS malformations and amount to about 1–2 cases per 1000 births. The incidence of intracranial abnormalities with an intact neural tube is uncertain as probably most of these escape detection at birth and only become manifest in later life. Long-term follow-up studies suggest however that the incidence may be as high as one in 100 births1. Ultrasound has been used for nearly 30 years as the main modality to help diagnose fetal CNS anomalies. The scope of these guidelines is to review the technical aspects of an optimized approach to the evaluation of the fetal brain in surveys of fetal anatomy, that will be referred to in this document as a basic examination. Detailed evaluation of the fetal CNS (fetal neurosonogram) is also possible but requires specific expertise and sophisticated ultrasound machines. This type of examination, at times complemented by three-dimensional ultrasound, is indicated in pregnancies at increased risk of CNS anomalies. In recent years fetal magnetic resonance imaging (MRI) has emerged as a promising new technique that may add important information in selected cases and mainly after 20–22 weeks, although its advantage over ultrasound remains debated
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".