Establishing a Canadian National Clinical Trials Network for Kidney Disease: Proceedings of a Planning Workshop
Bibliographic record
Abstract
Knowledge generation through randomized controlled trials (RCTs) is critical to advance the medical evidence base, inform decision-making, and improve care and outcomes. Unfortunately, nephrology has typically lagged behind other medical specialties in this regard. The establishment of formal clinical trial networks can facilitate the successful conduct of RCTs and has significantly increased the number of RCTs performed worldwide in other medical specialties. No such formal network of nephrology trialists exists in Canada. On April 24, 2014, the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET) Clinical Trials Committee held a stakeholder engagement meeting to address this gap and improve the nephrology clinical trial landscape in Canada. The meeting was held in Vancouver in association with the 2014 Canadian Society of Nephrology Annual General Meeting and was co-sponsored by the Kidney Foundation of Canada and CANN-NET. Attendees included nephrologists from university- and non-university-affiliated nephrology practices, administrators, and representatives from the Kidney Foundation of Canada. Through structured presentations and facilitated group discussions, the group explored the extent to which nephrology trials are currently happening in Canada, barriers to leading or participating in larger investigator-initiated trials, and strategies to improve clinical trial output in nephrology in Canada. The themes and action items arising from this meeting are discussed.
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 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 | 0.215 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 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".