Prospective head-to-head comparison of accuracy of two sequencing platforms for screening for fetal aneuploidy by cell-free DNA: the PEGASUS study
Bibliographic record
Abstract
We compared clinical validity of two non-invasive prenatal screening (NIPS) methods for fetal trisomies 13, 18, 21, and monosomy X. We recruited prospectively 2203 women at high risk of fetal aneuploidy and 1807 at baseline risk. Three-hundred and twenty-nine euploid samples were randomly removed. The remaining 1933 high risk and 1660 baseline-risk plasma aliquots were assigned randomly between four laboratories and tested with two index NIPS tests, blind to maternal variables and pregnancy outcomes. The two index tests used massively parallel shotgun sequencing (semiconductor-based and optical-based). The reference standard for all fetuses was invasive cytogenetic analysis or clinical examination at birth and postnatal follow-up. For each chromosome of interest, chromosomal ratios were calculated (number of reads for chromosome/total number of reads). Euploid samples' mean chromosomal ratio coefficients of variation were 0.48 (T21), 0.34 (T18), and 0.31 (T13). According to the reference standard, there were 155 cases of T21, 49 T18, 8 T13 and 22 45,X. Using a fetal fraction ≥4% to call results and a chromosomal ratio z-score of ≥3 to report a positive result, detection rates (DR), and false positive rates (FPR) were not statistically different between platforms: mean DR 99% (T21), 100%(T18, T13); 79%(45,X); FPR < 0.3% for T21, T18, T13, and <0.6% for 45,X. Both methods' negative predictive values in high-risk pregnancies were >99.8%, except for 45,X(>99.6%). Threshold analysis in high-risk pregnancies with different fetal fractions and z-score cut-offs suggested that a z-score cutoff to 3.5 for positive results improved test accuracy. Both sequencing platforms showed equivalent and excellent clinical validity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".