Typical Morphological Features on Prenatal Ultrasound of Fetuses With Trisomy 13 (Patau’s Syndrome)
Bibliographic record
Abstract
Background: Trisomy 13 (Patau's syndrome) is a devastating chromosomal abnormality with a poor prognosis. Fetal ultrasound is an effective screening method for early detection of trisomy 13. This study aimed to describe the typical morphological features detected on prenatal ultrasound in fetuses with trisomy 13 at Vietnam National Hospital of Obstetrics and Gynecology from 2012 to 2021. Methods: This was a retrospective, descriptive cross-sectional study of 50 fetuses with trisomy 13, compared to 4,166 normal fetuses. Maternal age and medical history were collected. Fetal ultrasound was performed in the first and second trimesters, and major structural abnormalities were recorded. The data were analyzed using descriptive statistics. Results: Trisomy 13 was detected in 98% of fetuses on ultrasound in the first and second trimesters. Of the 23 fetuses examined in the first trimester, 18 had increased nuchal translucency (NT ≥ 3 mm). The major structural abnormalities detected in fetuses with trisomy 13 included facial malformations (53.85%), brain anomalies (26.92%), heart defects (26.92%), abdominal wall abnormalities (23.08%), and kidney anomalies (26.92%). Nine cases of trisomy 13 (18%) were not detected on ultrasound. Conclusions: Increased NT and major structural abnormalities are suggestive signs for early screening of trisomy 13 by ultrasound. The combination of fetal ultrasound with other prenatal screening methods provides good results for the early detection of fetal abnormalities. This study provides important information on the typical morphological features detected on prenatal ultrasound in fetuses with trisomy 13, which can aid in counseling and decision-making for affected families. J Clin Gynecol Obstet. 2023;12(1):8-14 doi: https://doi.org/10.14740/jcgo848
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".