Public attitudes towards genomic risk profiling as a component of routine population screening
Bibliographic record
Abstract
Including low penetrance genomic variants in population-based screening might enable personalization of screening intensity and follow up. The application of genomics in this way requires formal evaluation. Even if clinically beneficial, uptake would still depend on the attitudes of target populations. We developed a deliberative workshop on two hypothetical applications (in colorectal cancer and newborn screening) in which we applied stepped, neutrally-framed, information sets. Data were collected using nonparticipant observation, free-text comments by individual participants, and a structured survey. Qualitative data were transcribed and analyzed using thematic content analysis. Eight workshops were conducted with 170 individuals (120 colorectal cancer screening and 50 newborn screening for type 1 diabetes). The use of information sets promoted informed deliberation. In both contexts, attitudes appeared to be heavily informed by assessments of the likely validity of the test results and its personal and health care utility. Perceived benefits included the potential for early intervention, prevention, and closer monitoring while concerns related to costs, education needs regarding the probabilistic nature of risk, the potential for worry, and control of access to personal genomic information. Differences between the colorectal cancer and newborn screening groups appeared to reflect different assessments of potential personal utility, particularly regarding prevention.
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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 | 0.041 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".