<p>Cancer Patient-Reported Preferences and Knowledge for Liquid Biopsies and Blood Biomarkers at a Comprehensive Cancer Center</p>
Bibliographic record
Abstract
BACKGROUND: Blood-based biomarkers (liquid biopsy) are increasingly used in precision oncology. Yet, little is known about cancer patients' perspectives in clinical practice. We explored patients' depth of preferences for liquid vs tissue biopsies and knowledge regarding the role of blood biomarkers on their cancer. METHODS: Three interviewer-administered trade-off scenarios and a 54-item self-administered questionnaire were completed by cancer outpatients across all disease sites at the Princess Margaret Cancer Centre. RESULTS: Of 413 patients, 54% were female; median age was 61 (range 18-101) years. In trade-off scenario preference testing, 90% (n=372) preferred liquid over tissue biopsy at baseline; when wait times for their preferred test were increased from 2 weeks, patients tolerated an additional mean of 1.8 weeks (SD 2.1) for liquid biopsy before switching to tissue biopsy (with wait time 2 weeks). Patients also tolerated a 6.2% decrease (SD 8.8) in the chance that their preferred test would conclusively determine optimal treatment before switching from the baseline of 80%. 216 patients (58%) preferred liquid biopsy even with no chance of adverse events from tissue biopsy. Patients' knowledge of blood-based biomarkers related to their cancer was low (mean 23%); however, the majority viewed development of blood biomarkers as important. CONCLUSION: Patients had limited understanding of cancer-specific blood-based biomarkers, but 90% preferred liquid over tissue biopsies to assess biomarkers. There was little tolerance to wait longer for results, or for decreased test-conclusiveness. Developing accurate, low-risk tests for cancer diagnosis and management for blood biomarkers is therefore desirable to patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".