Validation of a method for noninvasive prenatal testing for fetal aneuploidies risk and considerations for its introduction in the Public Health System
Bibliographic record
Abstract
Objective: The aim of this study was to validate noninvasive prenatal testing (NIPT) for fetal aneuploidies by whole-genome massively parallel sequencing (MPS).Methods: MPS was performed on cell-free DNA (cfDNA) isolated from maternal plasma in two groups: a first set of 186 euploid samples and a second set of 195 samples enriched of aneuploid cases (n = 69); digital PCR for fetal fraction (FF) assessment was performed on 178/381 samples. Cases with <10 × 106 reads (n = 54) were excluded for downstream data analysis. Follow-up data (invasive testing results or neonatal information) were available for all samples. Performances in terms of specificity/sensitivity and Z-score distributions were evaluated.Results: All positive samples for trisomy 21 (T21) (n = 43), trisomy 18 (T18) (n = 6) and trisomy 13 (T13) (n = 7) were correctly identified (sensitivity: 99.9%); 5 false positive results were reported: 3 for T21 (specificity = 98.9%) and 2 for T13 (specificity = 99.4%). Besides FF, total cfDNA concentration seems another important parameter for MPS, since it influences the number of reads.Conclusions: The overall test accuracy allowed us introducing NIPT for T21, T18 and T13 as a clinical service for pregnant women after 10 + 4 weeks of gestation. Sex chromosome aneuploidy assessment needs further validation due to the limited number of aneuploid cases in this study.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".