Ethical issues in pragmatic randomized controlled trials: a review of the recent literature identifies gaps in ethical argumentation
Bibliographic record
Abstract
BACKGROUND: Pragmatic randomized controlled trials (RCTs) are designed to evaluate the effectiveness of interventions in real-world clinical conditions. However, these studies raise ethical issues for researchers and regulators. Our objective is to identify a list of key ethical issues in pragmatic RCTs and highlight gaps in the ethics literature. METHODS: We conducted a scoping review of articles addressing ethical aspects of pragmatic RCTs. After applying the search strategy and eligibility criteria, 36 articles were included and reviewed using content analysis. RESULTS: Our review identified four major themes: 1) the research-practice distinction; 2) the need for consent; 3) elements that must be disclosed in the consent process; and 4) appropriate oversight by research ethics committees. 1) Most authors reject the need for a research-practice distinction in pragmatic RCTs. They argue that the distinction rests on the presumptions that research participation offers patients less benefit and greater risk than clinical practice, but neither is true in the case of pragmatic RCTs. 2) Most authors further conclude that pragmatic RCTs may proceed without informed consent or with simplified consent procedures when risks are low and consent is infeasible. 3) Authors who endorse the need for consent assert that information need only be disclosed when research participation poses incremental risks compared to clinical practice. Authors disagree as to whether randomization must be disclosed. 4) Finally, all authors view regulatory oversight as burdensome and a practical impediment to the conduct of pragmatic RCTs, and argue that oversight procedures ought to be streamlined when risks to participants are low. CONCLUSION: The current ethical discussion is framed by the assumption that the function of research oversight is to protect participants from risk. As pragmatic RCTs commonly involve usual care interventions, the risks may be minimal. This leads many to reject the research-practice distinction and question the need for informed consent. But the function of oversight should be understood broadly as protecting the liberty and welfare interest of participants and promoting public trust in research. This understanding, we suggest, will focus discussion on questions about appropriate ethical review for pragmatic RCTs.
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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 | 0.447 | 0.718 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".