Points-to-consider on the return of results in epigenetic research
Bibliographic record
Abstract
As epigenetic studies become more common and lead to new insights into health and disease, the return of individual epigenetic results to research participants, in particular in large-scale epigenomic studies, will be of growing importance. Members of the International Human Epigenome Consortium (IHEC) Bioethics Workgroup considered the potential ethical, legal, and social issues (ELSI) involved in returning epigenetic research results and incidental findings in order to produce a set of 'Points-to-consider' (P-t-C) for the epigenetics research community. These P-t-C draw on existing guidance on the return of genetic research results, while also integrating the IHEC Bioethics Workgroup's ELSI research on and discussion of the issues associated with epigenetic data as well as the experience of a return of results pilot study by the Personal Genome Project UK (PGP-UK). Major challenges include how to determine the clinical validity and actionability of epigenetic results, and considerations related to environmental exposures and epigenetic marks, including circumstances warranting the sharing of results with family members and third parties. Interdisciplinary collaboration and good public communication regarding epigenetic risk will be important to advance the return of results framework for epigenetic science.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".