Report on a Delphi Process and Workshop to Improve Accrual to Cancer Clinical Trials
Bibliographic record
Abstract
Cancer clinical trials (ccts) are essential for furthering knowledge and developing effective interventions to improve the lives of people living with cancer in Canada. Randomized controlled trials are particularly important for developing evidence-based health care interventions. To produce robust and relevant research conclusions, timely and sufficient accrual to ccts is essential. The present report delivers the key recommendations emerging from a workshop meeting, Improve Accrual to Cancer Clinical Trials, that was hosted by the Canadian Cancer Trials Group and funded by the Canadian Institutes of Health Research. The meeting, which took place in Toronto, Ontario, in April 2012 before the Canadian Cancer Trials Group annual spring meeting, brought together key stakeholders from across Canada to explore creative strategies for improving accrual to ccts. The objectives of the workshop were to provide an opportunity for knowledge exchange with respect to the research evidence and the ethics theory related to cct accrual and to promote discussion of best practices and policies related to enhancing cct access and accrual in Canada. The workshop provided the foundation for establishing new interdisciplinary research collaborations to overcome the identified barriers to cct participation in Canada. Meeting participants also supported the development of evidence-based policies and practices to make trials more accessible to Canadians living with cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".