Real-life implementation of prenatal cell-free DNA screening with in vitro fetal enrichment virtually eliminates the need for redraws and improves performance: A cohort study
Bibliographic record
Abstract
Purpose: Insufficient fetal fraction is a significant cause of prenatal cell-free DNA (cfDNA) screening failure, affecting 2% to 5% of samples, particularly among women with high body mass index (BMI). We evaluated the clinical impacts of in vitro fetal enrichment in a public prenatal cfDNA screening laboratory, hypothesizing that it would lower failure rates. Methods: This cohort study analyzed 8551 consecutive samples from pregnant women at an ISO15189-accredited prenatal cfDNA screening laboratory. We compared 4893 samples tested before and 3651 samples after implementing fetal enrichment. Samples were collected from January 2021 to October 2023 from high-risk (4809) pregnant women enrolled in the public Quebec Prenatal Screening Program (including 7 lost to follow-up and who were excluded from the analysis) and low-risk (3550) pregnancies from the Pegasus-2 project and divided into 4 groups. A total of 192 low-risk twin pregnancies were also included. Results: < .0001), enabling all women to receive a risk estimate at their first blood draw, even with a high BMI. It also improved clinical performance metrics. Conclusion: Prenatal cfDNA screening with in vitro fetal enrichment enhances accessibility and reliability of prenatal screening, nearly eliminating test failures and providing timely results for all samples, regardless of maternal BMI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".