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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it