Current controversies in prenatal diagnosis: Expanded NIPT that includes conditions other than trisomies 13, 18, and 21 should be offered
Bibliographic record
Abstract
Non-invasive prenatal testing (NIPT) based on analysis of cell free DNA circulating in the maternal plasma has been available clinically to screen for chromosomal abnormalities since 2011. There is significant evidence to suggest that NIPT has revolutionised prenatal screening for the common trisomies 13, 18, and 21. However, the evidence in favour of its extended use to screen for conditions other than these trisomies remains a topic of debate with no national or international organisation supporting clinical implementation for these indications. In the debate presented here - "Expanded NIPT that includes conditions other than trisomies 13, 18, and 21 should be offered" - we will see the pros and cons of screening for a wider range of chromosomal problems. The discussion presented swung the vote from 65% in favour and 35% against before the arguments were voiced to 41% in favour and 59% against. This significant swing in the vote indicates that the majority of our community feel more evidence is required before clinical implementation of extended NIPT.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".