Identification of trisomy 18, trisomy 13, and Down syndrome from maternal plasma
Bibliographic record
Abstract
Current prenatal diagnosis for fetal aneuploidies (including trisomy 21 [T21]) generally relies on an initial biochemical serum-based noninvasive prenatal testing (NIPT) after which women who are deemed to be at high risk are offered an invasive confirmatory test (amniocentesis or chorionic villi sampling for a fetal karyotype), which is associated with a risk of fetal miscarriage. Recently, genomics-based NIPT (gNIPT) was proposed for the analysis of fetal genomic DNA circulating in maternal blood. The diffusion of this technology in routine prenatal care could be a major breakthrough in prenatal diagnosis, since initial research studies suggest that this novel approach could be very effective and could reduce substantially the number of invasive procedures. However, the limitations of gNIPT may be underappreciated. In this review, we examine currently published literature on gNIPT to highlight advantages and limitations. At this time, the performance of gNIPT is relatively well-documented only in high-risk pregnancies for T21 and trisomy 18. This additional screening test may be an option for women classified as high-risk of aneuploidy who wish to avoid invasive diagnostic tests, but it is crucial that providers carefully counsel patients about the test's advantages and limitations. The gNIPT is currently not recommended as a first-tier prenatal screening test for T21. Since gNIPT is not considered as a diagnostic test, a positive gNIPT result should always be confirmed by an invasive test, such as amniocentesis or chorionic villus sampling. Validation studies are needed to optimally introduce this technology into the existing routine workflow of prenatal care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".