Cluster-randomized trials: A closer look
Bibliographic record
Abstract
BACKGROUND: The cluster-randomized trial is the methodology of choice for evaluating interventions administered at the group level such as public health and healthcare quality improvement interventions. Because of unique features of this design, it can be difficult to apply standard research ethics guidelines to cluster-randomized trials. The Ottawa Statement on the Ethical Design and Conduct of Cluster-Randomized Trials provides researchers and research ethics committees with comprehensive guidance on the ethical design, conduct and review of cluster-randomized trials. The Ottawa Statement supplements current national and international research ethics guidelines with guidance that is specific to cluster-randomized trials. In a recently published commentary, three examples drawn from the ClinicalTrials.gov registry were used to illustrate challenges associated with the cluster-randomized trial design. The commentary argued that the Ottawa Statement fails to provide comprehensive ethical guidance. In this article, we illustrate the application of the Ottawa Statement to the three trials. We challenge the conclusions reached in the commentary by demonstrating that an ethical analysis requires complete information. We correct some misperceptions about the cluster-randomized trial design. METHODS: We collected essential additional information by contacting the authors of trials and by referring to published trial articles. We used the Ottawa Statement to conduct an ethical analysis of each trial and to address a number of substantive concerns raised regarding the identification of study participants, informed consent and harm benefit analysis. RESULTS: In the two cases in which we were able to obtain detailed study information, we were able to complete the ethical analysis prescribed by the Ottawa Statement. CONCLUSION: The Ottawa Statement does provide a useful framework for the ethical design, review and conduct of cluster-randomized 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 | 0.384 | 0.608 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.025 | 0.048 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.031 | 0.068 |
| Insufficient payload (model declined to judge) | 0.008 | 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".