CRAFT—A Proposed Framework for Decentralized Clinical Trials Participation in Canada
Bibliographic record
Abstract
Canada's vast geography, and centralized delivery of cancer care and clinical trials create barriers for trial participation for patients in remote and rural settings. The development and implementation of a framework that enables safe and regulatory compliant trial participation through local healthcare providers would benefit Canadian patients, clinicians, trial sponsors and the health care system. To address this issue, representatives of Canada's cancer clinical trial community met to identify key challenges and develop recommendations for remote patient participation in trials. A structured literature review identified remote/rural trial delivery models. A panel of expert stakeholders reviewed the models and participated in a workshop to assess health system readiness, identify needed processes, tools and mechanisms, and develop recommendations for a Canadian framework for decentralized clinical trial conduct. The Canadian Remote Access Framework for clinical Trials (CRAFT) represents a risk-based approach used by site investigators to delegate responsibilities for a given trial to satellite health centres within a hub-and-spoke "trial cluster". The Framework includes specific recommendations to ensure research experience, capacity, regulatory compliance and patient safety. Canada's cancer care and telemedicine systems can be leveraged to enable broader access to clinical trials for patients who are geographically remote from cancer centres. CRAFT's risk-based framework is based on other successful models of remote trial patient management and is in the pilot implementation phase 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 | 0.106 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".