The epigenetic effects of assisted reproductive technologies: ethical considerations
Bibliographic record
Abstract
The use of assisted reproductive technologies (ART) has increased significantly, allowing many coping with infertility to conceive. However, an emerging body of evidence suggests that ART could carry epigenetic risks for those conceived through the use of these technologies. In accordance with the Developmental Origins of Health and Disease hypothesis, ART could increase the risk of developing late-onset diseases through epigenetic mechanisms, as superovulation, fertilization methods and embryo culture could impair the embryo's epigenetic reprogramming. Such epigenetic risks raise ethical issues for all stakeholders: prospective parents and children, health professionals and society. This paper focuses on ethical issues raised by the consideration of these risks when using ART. We apply two key ethical principles of North American bioethics (respect for autonomy and non-maleficence) and suggest that an ethical tension may emerge from conflicting duties to promote the reproductive autonomy of prospective parents on one hand, and to minimize risks to prospective children on the other. We argue that this tension is inherent to the entire enterprise of ART and thus cannot be addressed by individual clinicians in individual cases. We also consider the implications of the 'non-identity problem' in this context. We call for additional research that would allow a more robust evidence base for policy. We also call upon professional societies to provide clinicians with guidelines and educational resources to facilitate the communication of epigenetic risks associated with ART to patients, taking into consideration the challenges of communicating risk information whose validity is still uncertain.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".