What are the experiences of colorectal cancer patients with biomarker testing in Canada?: a mixed methods study
Bibliographic record
Abstract
OBJECTIVE: Molecular or biomarker testing to guide targeted treatments for colorectal cancer (CRC) has advanced care, specifically by improving treatment specificity. Our objective was to explore patients' experiences and perspectives with biomarker testing in Canada. METHODS: We conducted a mixed-methods study among adults (≥ 18 years) who have been diagnosed with CRC and able to communicate in English. Quantitative data was gathered using an online survey, with questions on awareness of and experiences with biomarker testing. Qualitative data was gathered using semi-structured interviews with a sample of survey respondents to provide context to survey findings. RESULTS: Among 55 survey respondents, 76% have heard of biomarker testing and of these, 67% have had biomarker testing done. Among the 33% of respondents that have not had biomarker testing done, reasons were: not offered/referred, fear/anxiety over results, and cost. Respondents who had biomarker testing largely found biomarker testing useful (89%), though, only half indicated that they were able to understand the information on their biomarker testing report. Qualitative analysis of interview transcripts identified four themes: 1) perceived benefits of biomarker testing, 2) knowledge of biomarker testing, 3) experiences with accessing and receiving biomarker testing, and 4) recommendations for addressing challenges with biomarker testing. CONCLUSION: Altogether, our study provides insight into CRC patients' perspectives and experiences with biomarker testing. Ongoing efforts by patient organizations, providers, and policymakers to improve awareness and access to biomarker testing must be informed by the patient perspective.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".