How does personal utility depend on clinical setting? Evidence from 3 cohorts
Bibliographic record
Abstract
Purpose: Evidence of personal utility of genetic testing is critical to clinical care, funding, and policy decisions. We aimed to understand how patient-oriented values and preferences for genetic testing vary across clinical settings to inform the development of a personal utility index. Methods: Participants were recruited from 3 clinical settings: pediatric clinical genetics, pediatric oncology, and prenatal care. For each cohort, a preliminary set of domains and elements of utility were generated from the literature. Semi-structured interviews were conducted with parents to understand the meaning of personal utility and relevance of preliminary domains and elements. Deductive coding identified shared and unique elements of utility across cohorts. Results: A total of 63 parents were interviewed. Personal utility domains that resonated with participants included cognitive, medical management, affective, behavioral, and social. Common elements included increased understanding about the cause of their child's condition and contributing to scientific knowledge. Unique elements were identified in each cohort: identifying support services in clinical genetics, understanding cancer risks in oncology, and pregnancy decision making in prenatal care. Conclusion: We identified shared and unique elements of personal utility across cohorts from 3 clinical settings, suggesting the need to tailor utility assessment to the clinical setting and patient population.
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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 | 0.074 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".