Practical steps to identifying the research risk of pragmatic trials
Bibliographic record
Abstract
BACKGROUND: Pragmatic randomized clinical trials that compare two or more purportedly "within the standard of care" interventions attempt to provide real-world evidence for policy and practice decisions. There is considerable debate regarding their research risk status, which in turn could lead to debates about appropriate consent requirements. Yet no practical guidance for identifying the research risks of pragmatic randomized clinical trials is available. METHODS: We developed a practical, four-step process for identifying and evaluating the research risk of pragmatic trials that can be applied to those pragmatic randomized clinical trials that compare two or more "standard of care" or "accepted" interventions. RESULTS: Using a variety of examples of standard of care pragmatic randomized clinical trials (ranging from trials comparing: insurance coverage conditions, patient reminders for health screens, intensive care unit procedures, post-stroke interventions, and drugs for life-threatening conditions), we illustrate in a four-step process how any pragmatic randomized clinical trial purportedly comparing standard interventions can be evaluated for their research risks. CONCLUSION: Although determining the risk status of a standard of care pragmatic randomized clinical trial is only one necessary element in the ethical oversight of such pragmatic randomized clinical trials, it is a central element. Our four-step process of pragmatic randomized clinical trial risk determination provides a practical, transparent, and systematic approach with likely low risk of bias.
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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 | 0.786 | 0.858 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.031 | 0.030 |
| Open science | 0.016 | 0.034 |
| Research integrity | 0.047 | 0.048 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".