Cancer, Clinical Trials, and Canada: Our Contribution to Worldwide Randomized Controlled Trials
Bibliographic record
Abstract
Canada has a long tradition of leading practice-changing clinical trials in oncology. Here, we describe methodology, results, and interpretation of oncology RCTs with Canadian involvement compared to RCTs from other high-income countries (HICs). A literature search identified all RCTs evaluating anti-cancer therapies published 2014–2017. RCTs were classified based on the country affiliation of first authors. The study cohort included 636 HIC-led RCTs; 155 (24%) had Canadian authors. Three-quarters (112/155, 72%) of Canadian RCTs were conducted in the palliative setting, compared to two thirds (299/481, 62%) of RCTs from other HICs (p = 0.022). Canadian RCTs were more likely than those from other HICs to be supported by industry (85% vs. 69%, p < 0.001). The proportion of positive Canadian trials that met the ESMO-MCBS threshold for substantial clinical benefit was comparable to RCTs without Canadian authors (29% vs. 32%, p = 0.137). Thirteen percent (20/155) of all Canadian trials were affiliated with the Canadian Cancer Trials Group (CCTG). Canada plays a meaningful role in the global cancer research ecosystem but is overly reliant on industry support. The very low proportion of trials that identify a new treatment with substantial clinical benefit is worrisome. A renewed investment in cancer clinical trials is needed in Canada.
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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 | 0.413 | 0.729 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.025 | 0.038 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.031 | 0.014 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".