Laboratory performance of genome-wide cfDNA for copy number variants as compared to prenatal microarray
Bibliographic record
Abstract
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".