Toward Timely and Equitable Advanced Biomarker Testing for Patients with Metastatic Cancer in Canada
Bibliographic record
Abstract
The explosion in biomarker testing over the past two decades continues to transform cancer care in Canada and around the world. Precision medicine is supported by identifying actionable mutations that direct therapeutic choices, thus improving survival and quality of life, especially for patients with advanced/metastatic disease. In addition, our growing understanding of the genetic basis of cancer is advanced by research employing ever-expanding databases of genetic mutations, therapies and outcomes. Despite this promising progress, however, access to biomarker testing remains inequitable across Canada, to the detriment of patients. Several underlying factors contribute to this situation, including the need for investment in and standardization of laboratory medicine infrastructure and processes, and the lack of suitable methods for cost/benefit evaluations to inform funding decisions. In 2024, a Canadian conference brought together patients, clinicians, researchers, policy-makers and scientists to address "Equitable Access to Advanced Biomarker Testing for Canadian Metastatic Cancer Patients". Two major themes arose from the conference: the urgent need to adopt comprehensive genomic profiling (CGP) as a standard of care across Canada, and the emerging role of liquid biopsy in accelerating access to biomarker testing for patients with advanced/metastatic cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".