The Outcome and Impact of Academic Cancer Clinical Trials with Participation from Canadian Sites (2015–2024)
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Academically sponsored cancer clinical trials (ACCTs) are essential for advancing patient-centered care, particularly in areas underserved by commercial research. The Canadian Cancer Clinical Trials Network (3CTN) was established to support high-quality multi-center ACCTs through coordinated infrastructure and funding. Over ten years, funders provided an average of CAD 4.3 million annually (~CAD 0.11 per capita), primarily from federal and provincial sources. This study evaluates the outcomes and impact of trials supported by 3CTN between 2015 and 2024. METHODS: We conducted a descriptive analysis of 350 ACCTs that stopped recruiting and had primary completion dates within the study period. Trial characteristics, results, publication rates, and incorporation into clinical guidelines were assessed using registry data, peer-reviewed publications, and structured searches of oncology guidelines. RESULTS: Among these 350 closed trials, 116 were Phase III studies. Of these, 36% were incorporated into clinical practice guidelines, and 7% were likely to be incorporated. Overall, 81% of trials were published in journals, and 45% posted results in public registries. Trials addressed diverse cancer types, with notable contributions in rare cancers and vulnerable populations. CONCLUSIONS: 3CTN-supported ACCTs had high completion and reporting rates, with substantial influence on clinical practice. These findings highlight how sustained infrastructure and modest public investment can deliver meaningful improvements in cancer care and inform evidence-based policy.
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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 | 0.184 | 0.478 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".