“I want to know what's in Pandora's box”: Comparing stakeholder perspectives on incidental findings in clinical whole genomic sequencing
Bibliographic record
Abstract
Whole genomic sequencing (WGS) promises significant personalized health benefits, and its increasingly low cost makes wide clinical use inevitable. However, a core challenge is "incidental findings" (IF). Using focus groups, we explored attitudes about the disclosure of IF in clinical settings from three perspectives: Genetics health-care professionals, the general public, and parents whose children have experienced genetic testing. Analysis was based on a framework approach. All three groups considered practical and ethical considerations. There was consensus that IF presented challenges for disclosure and a pre-test patient-clinician discussion was vital for clarification and agreement. The professionals favored targeted analysis to limit data handling and focus pre-test discussions on medical relevance. Their perspective highlighted ethical concepts of justice and beneficence. The lay groups' standpoint emphasized autonomy and patients' rights to choose what findings they receive, and that patients accept the consequences of any potential anxiety and uncertainty. The lay groups also felt that it was their responsibility to check genomic developments over time with their original test results and saw patient responsibility as an important part of patient choice.
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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 | 0.068 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".