Non-Invasive Prenatal Testing: Review of Ethical, Legal and Social Implications
Bibliographic record
Abstract
Non-invasive prenatal testing (NIPT) using cell-free fetal DNA (cffDNA) from maternal blood has recently entered clinical practice in many countries, including Canada. This test can be performed early during pregnancy to detect Down syndrome and other conditions. While NIPT promises numerous benefits, it also has challenging ethical, legal and social implications (ELSI). This paper reviews concerns currently found in the literature on the ELSI of NIPT. We make four observations. First, NIPT seems to exacerbate some of the already existing concerns raised by other prenatal tests (amniocentesis and maternal serum screening) such as threats to women’s reproductive autonomy and the potential for discrimination and stigmatization of disabled individuals and their families. This may be due to the likely upcoming large scale implementation and routinization of NIPT. Second, the distinction between NIPT as a screening test (as it is currently recommended) and as a diagnostic test (potentially in the future), has certain implications for the ELSI discussion. Third, we observed a progressive shift in the literature from initially including mostly conceptual analysis to an increasing number of empirical studies. This demonstrates the contribution of empirical bioethics approaches as the technology is being implemented into clinical use. Finally, we noted an increasing interest in equity and justice concerns regarding access to NIPT as it becomes more widely implemented.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".