Regional disparities in cancer biomarker knowledge and access across Canada.
Bibliographic record
Abstract
e13547 Background: Clinical trials are essential to the advancement of cancer therapies, yet accrual rates remain low. Multiple challenges contribute to the less than 5% enrollment rate of patients onto clinical trials. The Clinical Trials Navigator (CTN) program identified a key factor as the lack of biomarker knowledge across Canada. Methods: Between May 2024 and October 2024, an electronic survey was conducted among Canadian oncologists to assess the knowledge and accessibility of biomarkers in different regions across the country. A comprehensive biomarker list approved for funding by Cancer Care Ontario was used as a reference standard. Physicians were asked to identify accessible biomarkers from this list. Results: A total of 36 physicians responded to the survey, predominantly from Ontario (21), followed by British Columbia (7), Alberta (3), Manitoba (2), Québec (1), Nova Scotia (1), and Newfoundland and Labrador (NFL) (1). Regional biomarker knowledge varied. ER, PR and HER2 for breast cancer were reliably identified by 18/18 physicians. Colorectal cancer biomarkers also displayed high levels of knowledge and accessibility, with 16/16 physicians reporting awareness of MLH1, MSH2, and MSH6. Lung, hematological, and pancreatic cancers were also well represented. In contrast, biomarker knowledge and accessibility for adrenal, penile, and stomach cancers were substantially lower. Among these three cancer types, 11 physicians only identified 1 (HPV) out of the 6 available biomarkers (EBER for stomach, HPV for penile, MLH1, MSH2/6, PMS2 for adrenal), highlighting gaps in advanced testing knowledge. Geographic disparities in biomarker knowledge and access revealed significant variability across Canada. NFL reported the highest accessibility (86%), although this was based on one physician respondent, limiting generalizability. Ontario had the largest number of respondents (21) and reported an overall knowledge rate of 60%. This reflects educational gaps rather than accessibility, as all biomarkers in the survey were accessible. British Columbia (67%), Manitoba (77%), and Nova Scotia (85%) also demonstrated notable knowledge and accessibility, albeit with smaller sample sizes. Conversely, some provinces, including Alberta (54%), displayed the least overall biomarker knowledge and accessibility. Conclusions: Our survey identified significant physician-reported regional disparities in biomarker accessibility and knowledge across Canada. While certain biomarkers, such as those for breast and colorectal cancers, are reported to be widely accessible, gaps are evident in biomarker testing for rare cancers. Our findings illustrate the need for targeted educational initiatives and improved resource allocation to ensure equitable biomarker access nationwide. Enhanced knowledge and accessibility to biomarker testing can improve clinical trial enrollment rates, ultimately advancing cancer care outcomes.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".