A New Vision for Nephrology Trials in Canada
Bibliographic record
Abstract
Background: The Canadian Nephrology Trials Network (CNTN) was established in 2014 to improve the quantity and quality of clinical trials in nephrology in Canada. With inception of the Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD) Network in 2016, CNTN received additional funding to expand its mandate. We surveyed and assembled a broad cross-section of Canadian kidney patients, nephrology researchers, and other relevant stakeholders in order to establish an expanded new vision for CNTN. Methods: In July-August 2018, we administered two separate surveys - one to patient members of Can-SOLVE CKD and the second to members of CNTN and other Canadian nephrology investigators. We then conducted a two-day visioning workshop in September 2018 to discuss how best to support nephrology research in Canada. Over 40 stakeholders participated, including 10 patients, 22 researchers, and members of the Can-SOLVE CKD Operations Team. Results: Through the survey, we identified two issues that were at least moderately challenging: inability to facilitate multi-site trials (81%) and lack of engagement with community sites (74%). Three key themes emerged from the visioning exercise: peer review, training, and engagement. A summary report captured workshop discussions and was used to inform revisions to CNTN’s structure and governance. Three new working committees were created: Capacity Building, Communication and Engagement, and Scientific Operations; as well as a governing Executive Committee. Each committee is cochaired by a nephrologist and patient, who take turns leading the Executive Committee. Conclusions: With its new vision and committee structure, CNTN aims to promote a culture of collaboration within the Canadian kidney community and integrate patients into research. The network offers resources to enhance nephrology researchers’ ability to conduct clinical trials, directly involve patients in designing studies, and motivate change in patient care based on patient priorities through increased peer review, engagement, and training. Funding: Government Support - Non-U.S.
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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 | 0.175 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.041 | 0.016 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.018 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 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".