Willingness of patients to provide biologic samples for pharmacogenomic testing.
Bibliographic record
Abstract
6543 Background: Additional biopsies and/or blood sampling (i.e.: germline and plasma markers, circulating DNA or tumour cells) may be necessary to complete pharmacogenomic testing (PGT) in some patients. Cancer patients’ perspectives on providing additional biospecimens are important in addressing potential knowledge translation barriers. Methods: 790 clinic patients from the Princess Margaret Cancer Centre (Toronto, Canada) were interviewed with a standardized questionnaire, representing a wide distribution of adult solid and hematological disease sites. Study endpoints included patient preferences and willingness to provide biological samples (new blood, new biopsy, or pre-existing tumor samples) on a 5-point Likert scale. Univariate and multivariate models were created using SAS 9.3. Results: Patients were 49% female; 77% Caucasian/12% Asian; median age 58 years; and 67% had completed high school. Median household income was evenly trichotomized at $50K and $100K. Despite 33% of the patients being uncomfortable with the level of their knowledge about PGT, 75% agreed that additional tests would be beneficial to their health. Patient willingness to provide new samples was 88% for blood and 53% for biopsy, and 27% preferred existing tumor samples to be used in lieu of additional sampling. Caucasians and those with the highest incomes were more agreeable to providing blood and/or biopsy samples, whereas non-Caucasians and low-income earners preferred utilization of existing tumor samples over additional blood sampling (p<0.05). Conclusions: Patients were more willing to provide a new blood sample than a biopsy sample for PGT. Despite the vast majority agreeing to the potential benefit of additional sampling, only half were willing to provide a new biopsy sample. Level of willingness was related to sociocultural and economic factors. Improved patient education and increased efforts to design blood-based markers may improve uptake of PGT.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".