Cancer patient acceptance, understanding, and willingness to pay for pharmacogenetic testing (PGT).
Bibliographic record
Abstract
6005 Background: PGT offers the potential to improve cancer therapy through the use of specialized tests that can predict the level of efficacy and/or toxicity of specific treatments in an individual. However, there is currently little knowledge concerning cancer patient attitudes towards such testing in the clinical setting. Methods: We interviewed a broad cross-section of 278 cancer patients (20% lung, 19% breast, 20% colorectal, 40% other) using hypothetical time, efficacy, toxicity and willingness-to-pay trade-off PGT scenarios. Results: 153 potentially curable patients and 125 incurable patients received a separate series of trade-off scenarios. For curative patients, 70% accepted chemo that had a 5% absolute improvement in cure rate and <5% of severe toxicity. Of these, 99% wanted PGT where the test identifies a subset of patients benefiting from chemo; the same individuals were willing to pay a median $2,000 (range: $0-25,000) for PGT and would accept a median wait time for PGT results of 21 days (0-90). Patient preferences were insensitive to variation of fractions of individuals carrying the genetics associated with lack of benefit. In the incurable scenario, 90% of patients accepted palliative chemo with an 80% response rate and a severe side effect rate of 5%. Of these, 98% wanted PGT, where there test identifies individuals at highest risk of severe toxicity; the same individuals were willing to pay a median $1,000 ($0-15,000) for PGT, and would accept PGT turnaround times of 14 days (1-90). Patient preferences were insensitive to variation of fractions of individuals carrying the genetics associated with severe toxicity. The majority of patients (76% adjuvant; 87% metastatic) wanted to be involved in decision making regarding PGT; however, one in five patients (20% adjuvant; 22% metastatic) admitted that they lacked a basic understanding of what PGT means and its clinical implications. Conclusions: Among cancer patients willing to undergo chemo, almost all wanted PGT and were willing to pay for it, waiting several weeks for results. While patients had a strong desire to be involved in decision making for PGT, 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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".