Cultivating Innovative Pragmatic Cluster-Randomized Registry Trials Embedded in Hemodialysis Care: Workshop Proceedings From 2018
Bibliographic record
Abstract
Hemodialysis is a life-sustaining treatment for persons with kidney failure. However, those on hemodialysis still face a poor quality of life and a short life expectancy. High-quality research evidence from large randomized controlled trials is needed to identify interventions that improve the experiences, outcomes, and health care of persons receiving hemodialysis. With the support of the Canadian Institutes of Health Research and its Strategy for Patient-Oriented Research, the Innovative Clinical Trials in Hemodialysis Centers initiative brought together Canadian and international kidney researchers, patients, health care providers, and health administrators to participate in a workshop held in Toronto, Canada, on June 2 and 3, 2018. The workshop served to increase knowledge and awareness about the conduct of innovative, pragmatic, cluster-randomized registry trials embedded into routine hemodialysis care and provided an opportunity to discuss and build support for new trial ideas. The workshop content included structured presentations, facilitated group discussions, and expert panel feedback. Partnerships and promising trial ideas borne out of the workshop will continue to be developed to support the implementation of future large-scale trials.
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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 | 0.464 | 0.436 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 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".