Current controversy in prenatal diagnosis: The use of cfDNA to screen for monogenic conditions in low risk populations is ready for clinical use
Bibliographic record
Abstract
Abstract Noninvasive cfDNA testing for monogenic disorders (sgNIPT) has become integrated into the care of pregnant women at increased risk based on carrier status, known family history, or ultrasound anomalies. The availability of commercial tests for common autosomal recessive and de novo autosomal dominant conditions has led to the use of these tests in low‐risk pregnancies. However, is the technology ready for use in this low‐risk population? This report is a summary of the debate on this topic at the 27 th International Conference on Prenatal Diagnosis and Therapy. Both expert debaters provided strong arguments in favor and against the use of sgNIPT in low‐risk pregnancies. The argument in favor of sgNIPT for autosomal recessive conditions is that it allows the identification of affected pregnancies without the need for involving the partner in testing. Arguments for sgNIPT for autosomal dominant conditions include identification of affected fetuses that would have either presented later in pregnancy with fetal anomalies or not been detected prenatally given normal ultrasounds, respect for patient autonomy and patient desire for information. Strong arguments were made against offering sgNIPT screening. Given that traditional carrier screening for recessive conditions can be carried out in many jurisdictions, the added value of sgNIPT has not been clearly demonstrated. Arguments against sgNIPT for autosomal dominant conditions included the total lack of clinical validation studies and the risk of false reassurance in cases of negative results and unnecessary invasive procedures in cases of false positive results. Although there is a desire to take advantage of new technologies to improve the detection of monogenic disorders in low‐risk populations, based on the discussion and the audience vote, it appears premature to offer sgNIPT to all low risk pregnant women. Further clinical validation studies are needed prior to broad implementation.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".