Whether, when, how, and how much? General public’s and cancer patients’ views about the disclosure of genomic secondary findings
Bibliographic record
Abstract
Abstract Background:Data on the modalities of disclosing genomic secondary findings (SFs) remain scarce. We explore cancer patients’ and the general public’s perspectives about disclosing genomic SFs and the modalities of such disclosure. Methods:Sixty-one cancer patients (n=29) and members of the public (n=32) participated in eight focus groups in Montreal and Quebec City, Canada. They were asked to provide their perspectives of five fictitious vignettes related to medically actionable and non-actionable SFs. A thematic analysis of the transcripts was conducted. Results:Cancer patients and members of the public were open to receive genomic SFs, considering their potential clinical and personal utility. They believed that the right to know or not and share or not such findings should remain the patient’s decision. They thought that the disclosure of SFs should be made mainly in person by the prescribing clinician. Maintaining confidentiality when so requested and preventing genetic discrimination were considered essential. Conclusion:Participants in this study welcomed the prospect of disclosing genomic SFs, as long as the right to choose to know or not to know is preserved. Policies and practice guidelines aiming to protect genetic information confidentiality, and patients’ and families’ autonomy, physical and psychosocial wellbeing are expected.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".