Barriers and Unequal Access to Timely Molecular Testing Results: Addressing the Inequities in Cancer Care Delays across Canada
Bibliographic record
Abstract
Genomic medicine is a powerful tool to improve diagnosis and outcomes for cancer patients by facilitating the delivery of the right drug at the right dose at the right time for the right patient. In 2023, a Canadian conference brought together leaders with expertise in different tumor types. The objective was to identify challenges and opportunities for change in terms of equitable and timely access to biomarker testing and reporting at the education, delivery, laboratory, patient, and health-system levels in Canada. Challenges identified included: limited patient and clinician awareness of genomic medicine options with need for formal education strategies; failure by clinicians to discuss genomic medicine with patients; delays in or no access to hereditary testing; lack of timely reporting of results; intra- and inter-provincial disparities in access; lack of funding for patients to access testing and for laboratories to provide testing; lack of standardized testing; and impact of social determinants of health. Canada must standardize its approach to biomarker testing across the country, with a view to addressing current inequities, and prioritize access to advanced molecular testing to ensure systems are in place to quickly bring innovation and evidence-based treatments to Canadian cancer patients, regardless of their place of residence or socioeconomic status.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".