Cancer patients’ and physicians’ preferences for decision making regarding pharmacogenomic testing (PGT).
Bibliographic record
Abstract
13 Background: Pharmacogenomics is increasingly utilized in oncology; however, there is little knowledge concerning cancer patients’ or oncologists’ attitudes toward PGT decision-making in clinical practice. Methods: A broad cross-section of cancer patients were interviewed regarding their attitudes toward PGT using hypothetical time, efficacy and toxicity trade-off and willingness-to-pay scenarios (N=278) and/or quantitative choice-based conjoint analysis surveys (N=264); 64 cancer specialists/physicians in training were surveyed similarly. Results: Of patients participating in the trade-off scenario phase of study, >94% accepted chemotherapy, and of these, >98% wanted PGT that identifies a subset of patients either benefiting from chemotheraphy or who were at risk of severe toxicity. Patients were willing to pay between CAD $1,000-$1,900 for PGT and accept wait times for results of 2-3 weeks. Similar findings were observed in the conjoint phase of the study, with preferences for PGT starting to decline when the out-of-pocket costs reached CAD $500-$1,500, wait time for results exceeded 14 days, and when the prevalence of the genetic variant fell below 25%. Adjuvant patients’ acceptance of PGT was most influenced by cost (decision weight [DW]=41%) and prevalence of the genetic variant associated with lack of benefit from chemo (DW=26%). Metastatic patients were most influenced by cost (DW=49%) and wait times (DW=31%). More patients reported difficulty understanding conjoint surveys (14%) than trade-off scenarios (7%; p=0.01). 81% of patients wanted to be involved in decision-making regarding PGT; while 30% of physicians felt it should be a physician-only decision (p=0.006). However, 21% of patients and 5% of physicians admitted to not understanding PGT, while just 14% of physicians rated themselves as very knowledgeable regarding PGT. Conclusions: Cancer patients overwhelmingly accept and want to be involved in decision-making regarding PGT, to a greater extent than what physicians prefer. However, communication of PGT information was a potential barrier, as a considerable minority lacked the necessary knowledge to facilitate informed decision-making. Improved patient and physician education is necessary.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".