Navigating the consent river: questions to consider before waiving consent requirements in pragmatic cluster randomised trials
Bibliographic record
Abstract
The robust design and conduct of pragmatic cluster randomised trials may be in tension with the ethical requirement to obtain written informed consent from prospective research participants. In our experience, researchers tend to focus on whether a waiver of consent is appropriate for their studies. However, pragmatic cluster randomised trials raise other important questions that have direct implications for determining when an alteration or waiver of consent is permissible. To assist those involved in the design, conduct and review of pragmatic cluster randomised trials, we outline four critical questions to consider: (1) What is the nature of the intervention being evaluated? (2) Is the choice to use cluster randomisation justified? (3) Can the risk of recruitment bias be addressed? and (4) Is an alteration or waiver of consent appropriately justified? We recommend that researchers and research ethics committees conduct a stepwise analysis of a planned cluster randomised trial using these questions. To illustrate the application of this stepwise analysis, we use three pragmatic cluster randomised trials in the haemodialysis setting as case studies.
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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 | 0.216 | 0.830 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.027 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".