The Rethinking Clinical Trials (REaCT) Program: A Pragmatic Research Strategy to Improve Cancer Care for Patients, Caregivers, and Healthcare Systems
Bibliographic record
Abstract
Cancer care has become increasingly complex, expensive, and inaccessible, with patients often exposed to increased treatment-related harms for marginal benefits. Pragmatic clinical trials offer a solution by conducting real-world studies that evaluate dose optimization, toxicity, quality of life, and resource utilization. Pragmatic trials can also address the efficacy-effectiveness gap: the poorer outcomes and greater toxicity observed in everyday practice compared to those reported in many clinical trials. The Rethinking Clinical Trials (REaCT) program was designed to conduct patient-centered practice-changing research by involving patients, their families, and healthcare providers in the design of inclusive, real-world clinical trials. The REaCT process starts with surveys and systematic reviews to identify knowledge gaps and uses this information to design pragmatic clinical trials that address these deficits. Since 2014, the program has conducted 17 patient and 17 healthcare provider surveys with 2298 and 1033 responses, respectively. With these results, the program has performed 22 systematic reviews. These surveys and systematic reviews have resulted in 19 completed and 8 ongoing REaCT clinical trials that have recruited over 5000 patients from across Canada. Here, we present some of the practice-changing research conducted by the REaCT program and address challenges facing the growth of pragmatic research.
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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 | 0.653 | 0.646 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".