Mapping ethical, legal, and social implications (ELSI) of preimplantation genetic testing (PGT)
Bibliographic record
Abstract
PURPOSE: Preimplantation Genetic Testing (PGT) has attracted considerable ethical, legal, and social scrutiny, but academic debate often fails to reflect clinical realities. METHODS: Addressing this disconnect, a review of 506 articles from 1999 to 2019 across humanities and social sciences was conducted to synthesize the Ethical, Legal, and Social Implications (ELSI) of PGT. This review mined PubMed, WoS, and Scopus databases, using both MeSH terms and keywords to map out the research terrain. RESULTS: The findings reveal a tenfold increase in global research output on PGT's ELSI from 1999 to 2019, signifying rising interest and concern. Despite heightened theoretical discourse on selecting "optimal" offspring, such practices were scarcely reported in clinical environments. Conversely, critical issues like PGT funding and familial impacts remain underexplored. Notably, 86% of the ELSI literature originates from just 12 countries, pointing to a research concentration. CONCLUSION: This review underscores an urgent need for ELSI research to align more closely with clinical practice, promoting collaborations among ethicists, clinicians, policymakers, and economists. Such efforts are essential for grounding debates in practical relevance, ultimately steering PGT towards ethical integrity, societal acceptance, and equitable access, aiming to harmonize PGT research with real-world clinical concerns, enhancing the relevance and impact of future ethical discussions.
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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 | 0.052 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".