Developing a Model of a Patient-Group Pathway to Accessing Cancer Clinical Trials in Canada
Bibliographic record
Abstract
Background: Colorectal Cancer Canada, in partnership with a Scientific Advisory Committee, is developing a Canadian Patient Group Pathway to Accessing Cancer Clinical Trials ("Pathway"). A central element of the Pathway is presented here-namely, a set of recommendations and tools aimed at each stakeholder group. Methods: A summary of the peer-reviewed and grey literature informed discussions at a meeting, held in June 2017, in which a cross-section of stakeholders reached consensus on the potential roles of patient groups in the cancer clinical trials process, barriers to accessing cancer clinical trials, best practice models for patient-group integration, and a process for developing the Pathway. Canadian recommendations and tools were subsequently developed by a small working group and reviewed by the Scientific Advisory Committee. Results: The major output of the consensus conference was agreement that the Clinical Trials Transformation Initiative (ctti) model, successfully applied in the United States, could be adapted to create a Canadian Pathway. Two main differences between the Canadian and American cancer clinical research environments were highlighted: the effects of global decision-making and systems of regulatory and funding approvals. The working group modified the ctti model to incorporate those aspects and to reflect Canadian stakeholder organizations and how they currently interact with patient groups. Conclusions: Developing and implementing a Canadian Pathway that incorporates the concepts of multi-stakeholder collaboration and the inclusion of patient groups as equal partners is expected to generate significant benefits for all stakeholders. The next steps to bring forward a proposed Pathway will involve engaging the broader cancer research community. Clinical trial sponsors will be encouraged to adopt a Charter recognizing the importance of including patient groups, and to support the training of patient groups through an independent body to ensure quality research partners. Integration of patient groups into the process of developing "real world" evidence will be advanced by a further consensus meeting being organized by Colorectal Cancer Canada for 6-7 November 2018.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".