Cancer patients’ acceptance, understanding, and willingness-to-pay for pharmacogenomic testing
Bibliographic record
Abstract
BACKGROUND: Pharmacogenomics is gaining increasing importance in the therapeutics of cancer; yet, there is little knowledge of cancer patients' attitudes toward the use of pharmacogenomic testing in clinical practice. We carried out this study to explore cancer patients' acceptance, understanding, and willingness-to-pay for pharmacogenomic testing. MATERIALS AND METHODS: A broad cross-section of gastrointestinal, lung, breast, and other cancer patients were interviewed in terms of their acceptance of pharmacogenomic testing using hypothetical time, efficacy, and toxicity trade-off and willingness-to-pay scenarios. RESULTS: Among the 96% of 123 adjuvant patients accepting chemotherapy under optimal conditions, 99% wanted pharmacogenomic testing that could identify a subset of patients benefiting from chemotherapy, accepting median incurred costs of $2000 (range $0-25,000) and turnaround time for test results of 16 days (range 0-90 days). Among the 97% of 121 metastatic patients accepting chemotherapy, 97.4% wanted pharmacogenomic testing that could detect the risk of severe toxicity, accepting median incurred costs of $1000 (range $0-10,000) and turnaround time for results of 14 days (range 1-90 days). The majority of patients wanted to be involved in decision-making on pharmacogenomic testing; however, one in five patients lacked a basic understanding of pharmacogenomic testing. CONCLUSION: Among cancer patients willing to undergo chemotherapy, almost all wanted pharmacogenomic testing and were willing-to-pay for it, waiting several weeks for results. Although patients had a strong desire to be involved in decision-making on pharmacogenomic testing, a considerable proportion lacked the necessary knowledge to make informed choices.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".