Use of iPad technology to determine cancer patient-reported preferences for and understanding of pharmacogenetic testing (PGT).
Bibliographic record
Abstract
319 Background: PGT in oncology can be used to predict the efficacy and toxicity of a particular treatment in an individual. Previous work by our group has demonstrated that among cancer patients willing to undergo chemotherapy, >98% wanted PGT testing if it could identify patients who would respond to chemo. However, in the original study using a paper questionnaire, 22% of patients did not understand the concept of PGT and its clinical implications. Therefore, we have devised a simpler, more visual questionnaire in electronic format using iPad technology and simple animations. We are assessing if patient understanding using this format is increased and also if patients prefer completing the survey with this novel technology. Methods: An interim analysis of a broad cross-section of cancer patients using an iPad was performed. PGT questions related to hypothetical efficacy, toxicity, time to test results, willingness to pay as well as understanding of PGT scenarios were assessed. Results: 135 cancer patients (87% adjuvant, 12% metastatic; 27% breast, 25% colon, 22% heme malignancy, 23% other) attending Princess Margaret Hosptial participated. 85% of patients accepted chemo that had a 5% absolute improvement in survival and <10% chance of side effects. 94% of patients chose to have PGT if it could identify subsets of patients who would benefit from chemo. The median that patients were willing to pay for PGT was $250 (range $0-$5,000), however the median that patients felt was a reasonable price was $100 ($0-$5,000). 11% of patients admitted that they did not understand the concept of PGT and its clinical implications. Conclusions: Almost all patients who were willing to undergo chemo were also willing to accept PGT. Fewer patients in this study compared with our original paper directed questionnaire described themselves as lacking an understanding of PGT (11% versus 22% respectively), suggesting that survey administration on an iPad with a visual characterization of question scenarios improved understanding. Final data from over 300 patients will be analyzed in the fall, which would include data on patient attitudes on use of such technology for communication and decision making.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".