Value of Genetic Testing for Hereditary Colorectal Cancer in a Probability-Based US Online Sample
Bibliographic record
Abstract
BACKGROUND: . While choices about genetic testing are increasingly common for patients and families, and public opinion surveys suggest public interest in genomics, it is not known how adults from the general population value genetic testing for heritable conditions. We sought to understand in a US sample the relative value of the characteristics of genetic tests to identify risk of hereditary colorectal cancer, among the first genomic applications with evidence to support its translation to clinical settings. METHODS: . A Web-enabled choice-format conjoint survey was conducted with adults age 50 years and older from a probability-based US panel. Participants were asked to make a series of choices between 2 hypothetical blood tests that differed in risk of false-negative test, privacy, and cost. Random parameters logit models were used to estimate preferences, the dollar value of genetic information, and intent to have genetic testing. RESULTS: . A total of 355 individuals completed choice-format questions. Cost and privacy were more highly valued than reducing the chance of a false-negative result. Most (97% [95% confidence interval (CI)], 95%-99%) would have genetic testing to reduce the risk of dying of colorectal cancer in the best scenario (no false negatives, results disclosed to primary care physician). Only 41% (95% CI, 25%-57%) would have genetic testing in the worst case (20% false negatives, results disclosed to insurance company). CONCLUSIONS: . Given the characteristics and levels included in the choice, if false-negative test results are unlikely and results are shared with a primary care physician, the majority would have genetic testing. As genomic services become widely available, primary care professionals will need to be increasingly knowledgeable about genetic testing decisions.
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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 | 0.010 |
| 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".