Canadian Research Ethics Board Leadership Attitudes to the Return of Genetic Research Results to Individuals and Their Families
Bibliographic record
Abstract
The return of individual genetic results to research participants has been widely discussed in the context of an explosion of genetic research utilizing an ever more rapid and inexpensive array of sequencing and bioinformatics platforms. To date, a number of consensus statements guide researchers as to the breadth and limits of their obligations for offering genomic research results to participants. Typically these recommendations are rooted in the result’s clinical validity, actionability, and potential health consequences, and are predicated on the informed consent of the participant. An emerging discussion is the challenging question of the degree to which researchers may additionally have responsibility for offering results to family members of the research participant. Some have argued that ethical obligations to relatives intensify as the significance and actionability of the result increase, while others claim that obligations to next of kin should follow the clinical model where the decision to share genetic results falls to the patient. A detailed reflection on the many ethical issues that arise in considering whether such a responsibility exists, and if so how to honor it, is presented in this issue of JLME by Wolf et al.
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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 | 0.121 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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".