The Outcome and Impact of Academic Cancer Clinical Trials with Participation from Canadian Sites (2015–2024)
Bibliographic record
Abstract
Background/Objectives: Academic sponsored cancer clinical trials (ACCTs) are essential for advancing patient-centered care, particularly in areas underserved by commercial re-search. In Canada, the Canadian Cancer Clinical Trials Network (3CTN) was established to support high-quality multi-centre ACCTs. This study evaluates the outcomes and im-pact of trials supported by 3CTN between 2015 and 2024. Methods: We conducted a descriptive analysis of 350 ACCTs that were closed to recruitment and had primary completion dates within the study period. Trial characteristics, results, publication rates, and in-corporation into clinical guidelines were assessed using registry data, peer-reviewed publications, and structured searches of oncology guidelines. Results: Among the 350 complete 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 the importance of sustained infrastructure and funding ACCTs and their role in improving cancer care. The impact achieved with relatively modest investment from public funders underscores the value of sustained investment in investigator-led research and coordinated network support.
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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 | 0.251 | 0.542 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".