Ethical Issues in Pragmatic Cluster-Randomized Trials in Dialysis Facilities
Bibliographic record
Abstract
A pragmatic cluster-randomized trial (CRT) is a research design that may be used to efficiently test promising interventions that directly inform dialysis care. While the Ottawa Statement on the Ethical Design and Conduct of Cluster Randomized Trials provides general ethical guidance for CRTs, the dialysis setting raises additional considerations. In this article, we outline ethical issues raised by pragmatic CRTs in dialysis facilities. These issues may be divided into 7 key domains: justifying the use of cluster randomization, adopting randomly allocated individual-level interventions as a facility standard of care, conducting benefit-harm analyses, gatekeepers and their responsibilities, obtaining informed consent from research participants, patient notification, and including vulnerable participants. We describe existing guidelines relevant to each domain, illustrate how they were considered in the Time to Reduce Mortality in End-Stage Renal Disease (TiME) trial (a prototypical pragmatic hemodialysis CRT), and highlight remaining areas of uncertainty. The following is the first step in an interdisciplinary mixed-methods research project to guide the design and conduct of pragmatic CRTs in dialysis facilities. Subsequent work will expand on these concepts and when possible, argue for a preferred solution.
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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 | 0.782 | 0.811 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.027 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".