Societal preferences for the return of incidental findings from clinical genomic sequencing: a discrete-choice experiment
Bibliographic record
Abstract
BACKGROUND: An important challenge with the application of next-generation sequencing technology is the possibility of uncovering incidental genomic findings. A paucity of evidence on personal utility for incidental findings has hindered clinical guidelines. Our objective was to estimate personal utility for complex information derived from incidental genomic findings. METHODS: We used a discrete-choice experiment to evaluate participants' personal utility for the following attributes: disease penetrance, disease treatability, disease severity, carrier status and cost. Study participants were drawn from the Canadian public. We analyzed the data with a mixed logit model. RESULTS: In total, 1200 participants completed our questionnaire (available in English and French). Participants valued receiving information about high-penetrance disorders but expressed disutility for receiving information on low-penetrance disorders. The average willingness to pay was $445 (95% confidence interval [CI] $322-$567) to receive incidental findings in a scenario where clinicians returned information about high-penetrance, medically treatable disorders, but only 66% of participants (95% CI 63%-71%) indicated that they would choose to receive information in that scenario. On average, participants placed an important value ($725, 95% CI $600-$850) on having a choice about what type of findings they would receive, including receipt of information about high-penetrance, treatable disorders or receipt of information about high-penetrance disorders with or without available treatment. The predicted uptake of that scenario was 76% (95% CI 72%-79%). INTERPRETATION: Most participants valued receiving incidental findings, but personal utility depended on the type of finding, and not all participants wanted to receive incidental results, regardless of the potential health implications. These results indicate that to maximize benefit, participant-level preferences should inform the decision about whether to return incidental findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".