Geographic Disparities in Access to Cancer Clinical Trials in Canada
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate geographic disparities in access to cancer clinical trials across Canada. METHODS: Cancer clinical trial data recorded within the clinicaltrials.gov and reporting the conduct of any of these trials in Canada, 2005 to 2023 were reviewed. Frequency analyses of the number of clinical trials that were registered on clinicaltrials.gov for Canada, individual Canadian provinces, main Canadian urban centers, and different cancer types, according to the funding source (industry versus non-industry), as well as according to different periods (using 3-y intervals) were conducted. Moreover, a comparison of cancer clinical trials per 10,000 persons was done between Canada and the United States. RESULTS: The number of cancer clinical trials per 10,000 individuals (according to the 2021 census) in each province/territory varied between 6.79 (New Brunswick) to 0 (the 3 territories). The number of cancer clinical trials in relation to 1000 projected cancer cases for some of the common tumor types in Canada was then reviewed. The highest number was for lymphoma clinical trials (32.85), whereas the lowest number was for bladder cancer clinical trials (7.06). Most of the trials have industry funding (69%). Using 3-year intervals, the highest number of cancer clinical trials was observed from 2014 to 2016 (778 trials), and the lowest number was observed from 2020 to 2022 (633 trials). CONCLUSIONS: Access to clinical trials in Canada is not equitably distributed, with geographical and primary tumor site disparities. Moreover, access to cancer clinical trials has been negatively impacted during the time of the COVID-19 pandemic.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".