Noninvasive Prenatal Testing: Implications for Muslim Communities
Bibliographic record
Abstract
Noninvasive prenatal testing (NIPT), a new technology that uses cell-free fetal DNA in maternal plasma to detect fetal aneuploidies, is currently entering clinical practice. NIPT offers great clinical benefits as it is much more accurate than current screening, allows earlier testing, and eliminates the risk of miscarriage associated with amniocentesis and chorionic villus sampling (CVS). In the future, it may become accurate enough to replace invasive testing. To date, no attention has been given in the literature to the role that earlier testing through NIPT might play in the context of religious traditions with particular attitudes toward abortion, such as in Muslim communities. This article presents some Islamic views regarding fetal development and abortion as well as some recent legislative developments surrounding abortion for fetal conditions, focusing on Iran and Saudi Arabia. It then offers a discussion of possible implications of NIPT for Muslim communities, such as its potential to allow access to diagnostic information before “ensoulment,” the point of fetal development at which the fetus is bestowed the moral status of a human being. The possible impact of early access to information on the evolution of legislation surrounding abortion for fetal conditions is also discussed. Finally, we suggest future research directions based on empirical studies with women, partners, health professionals, and religious authorities in Muslim countries and in countries with a Muslim majority in order to explore their preferences and attitudes regarding the future implementation of NIPT within those communities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".