Clinical application of noninvasive prenatal testing for the detection of trisomies 21, 18, and 13: a hospital experience
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to report the clinical application of noninvasive prenatal testing (NIPT) to detect chromosomal aneuploidies, especially trisomies 21, 18, and 13 in Chinese singleton pregnancies. METHODS: Pilot validation between NIPT with full karyotyping was conducted blindly on 306 cases. Subsequently, 7705 pregnancies were offered with NIPT. Follow-up data was obtained in all cases. RESULTS: In the validation stage, a total of five NIPT positive cases were observed for trisomies 21 and 18, and results were confirmed by karyotyping; there were no cases of trisomy 13. Thus, the overall sensitivity and specificity in the validation stage was 100%. In 7705 cases, NIPT results were obtained in 7701 cases; 66 cases were classified as positive, including 48 cases of trisomy 21, 14 cases of trisomy 18, and 4 cases of trisomy 13. Subsequent karyotyping documented two false positive diagnoses for trisomies 21, 18, and 13, respectively. Sensitivity and specificity for detection of trisomies 21 and 18 and 13 were 100% and 99.9%, respectively. Additionally, prenatal chromosomal detection for pregnancies with NIPT has shown a gradual increase since its implementation. CONCLUSION: Noninvasive prenatal testing allows a more suitable and efficient workflow for our patients' needs, together with invasive procedures allows a higher prenatal detection of chromosomal aneuploidies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".