Attitudes of Members of Genetics Professional Societies Toward Human Gene Editing
Bibliographic record
Abstract
Abstract Gene-editing technologies have improved in ease, efficiency, and precision. Although discussions are occurring around acceptable uses of human gene editing, limited data exist on the views of genetics-trained individuals. In 2017, we distributed an anonymous online survey to assess the attitudes of members of genetics professional societies toward gene editing ( N = 500). Virtually all respondents were supportive of somatic editing in basic-science (99.2%) and clinical (87.4%) research on nonreproductive human cells. Only 57.2% were supportive of germline-editing basic-science research; 31.9% supported the transfer of viable embryos to humans for clinical research. While most favored future therapeutic uses of somatic (96.6%) and germline (77.8%) editing, there was little support for enhancement in somatic (13.0%) or germline (8.6%) cells. This study describes attitudes toward gene editing from genetics professionals worldwide and contributes to ongoing discourse and policy guidance in this domain.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".