Ethical and regulatory issues of pragmatic cluster randomized trials in contemporary health systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cluster randomized trials randomly assign groups of individuals to examine research questions or test interventions and measure their effects on individuals. Recent emphasis on quality improvement, comparative effectiveness, and learning health systems has prompted expanded use of pragmatic cluster randomized trials in routine health-care settings, which in turn poses practical and ethical challenges that current oversight frameworks may not adequately address. The 2012 Ottawa Statement provides a basis for considering many issues related to pragmatic cluster randomized trials but challenges remain, including some arising from the current US research and health-care regulations. In order to examine the ethical, regulatory, and practical questions facing pragmatic cluster randomized trials in health-care settings, the National Institutes of Health Health Care Systems Research Collaboratory convened a workshop in Bethesda, Maryland, in July 2013. Attendees included experts in clinical trials, patient advocacy, research ethics, and research regulations from academia, industry, the National Institutes of Health Collaboratory, and other federal agencies. Workshop participants identified substantial barriers to implementing these types of cluster randomized trials, including issues related to research design, gatekeepers and governance in health systems, consent, institutional review boards, data monitoring, privacy, and special populations. We describe these barriers and suggest means for understanding and overcoming them to facilitate pragmatic cluster randomized trials in health-care settings.
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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 | 0.819 | 0.949 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it