Cancer patient-reported knowledge and preferences for liquid biopsies and blood biomarkers at a comprehensive cancer centre.
Bibliographic record
Abstract
6587 Background: Novel blood-based biomarkers, including cell-free DNA and plasma signatures, are becoming a reality in precision oncology. Yet, little is known about cancer patients’ perspectives on blood biomarkers in clinical practice. Methods: A 54-item self-administered questionnaire and four interviewer-administered trade-off scenarios were administered to cancer patients across all sites at Princess Margaret Cancer Centre. Results: Of 632 eligible patients, 66% (n = 417) completed the survey; 54% female; median age 61 (range 18-101) years. Patients had a median accuracy score of 18% (range 0-81%) on their knowledge of the role of biomarkers on their own cancer. Disease site was significantly associated with knowledge (P = 0.029); patients with breast, genitourinary, and thoracic cancers performed better than patients of other sites. Females (P = 0.012) and those with higher education (P = 0.019) and income (P = 0.0016) also scored better. Scores were not associated with the stage at diagnosis, time since diagnosis, age or ethnicity. Using chart review, 91% had been evaluated in at least one setting with either tissue, blood, or clinical biomarkers; however, only 20% of them were aware of this. In the scenario-based preference testing, if given a choice, 90% (n = 372) preferred a liquid (blood) over a tissue biopsy; however, these patients only accepted a median waiting period of one additional week (IQR: 0-3 weeks) and a 5% decrease (IQR 0-10%) in sensitivity of identifying the right treatment before switching their preference to the tissue biopsy. The majority (n = 216; 58%) were not interested in switching even with no potential complications from tissue biopsy. People with higher education were more likely to switch based on the level of risk (P < 0.001). Conclusions: Although patients had limited understanding of their cancer-specific blood-based biomarkers, 90% preferred blood over tissue biomarkers, but with little tolerance to waiting longer for results or decreased test sensitivity. Developing blood biomarkers and performing liquid biopsies are therefore desirable to patients, but only if they had similar or improved test characteristics over their tissue counterparts.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".