Laboratory performance of genome-wide cfDNA for copy number variants as compared to prenatal microarray
Bibliographic record
Abstract
BACKGROUND: Noninvasive prenatal testing (NIPT) allows for screening of fetal aneuploidy and copy number variants (CNVs) from cell-free DNA (cfDNA) in maternal plasma. Professional societies have not yet embraced NIPT for fetal CNVs, citing a need for additional performance data. A clinically available genome-wide cfDNA test screens for fetal aneuploidy and CNVs larger than 7 megabases (Mb). RESULTS: This study reviews 701 pregnancies with "high risk" indications for fetal aneuploidy which underwent both genome-wide cfDNA and prenatal microarray. For aneuploidies and CNVs considered 'in-scope' for the cfDNA test (CNVs ≥ 7 Mb and select microdeletions), sensitivity and specificity was 93.8% and 97.3% respectively, with positive and negative predictive values of 63.8% and 99.7% as compared to microarray. When including 'out-of-scope' CNVs on array as false negatives, the sensitivity of cfDNA falls to 48.3%. If only pathogenic out-of-scope CNVs are treated as false negatives, the sensitivity is 63.8%. Of the out-of-scope CNVs identified by array smaller than 7 Mb, 50% were classified as variants of uncertain significance (VUS), with an overall VUS rate in the study of 2.29%. CONCLUSIONS: While microarray provides the most robust assessment of fetal CNVs, this study suggests that genome-wide cfDNA can reliably screen for large CNVs in a high-risk cohort. Informed consent and adequate pretest counseling are essential to ensuring patients understand the benefits and limitations of all prenatal testing and screening options.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".