The ethical challenges raised in the design and conduct of pragmatic trials: an interview study with key stakeholders
Bibliographic record
Abstract
BACKGROUND: There is a concern that the apparent effectiveness of interventions tested in clinical trials may not be an accurate reflection of their actual effectiveness in usual practice. Pragmatic randomized controlled trials (RCTs) are designed with the intent of addressing this discrepancy. While pragmatic RCTs may increase the relevance of research findings to practice they may also raise new ethical concerns (even while reducing others). To explore this question, we interviewed key stakeholders with the aim of identifying potential ethical challenges in the design and conduct of pragmatic RCTs with a view to developing future guidance on these issues. METHODS: Interviews were conducted with clinical investigators, methodologists, patient partners, ethicists, and other knowledge users (e.g., regulators). Interviews covered experiences with pragmatic RCTs, ethical issues relevant to pragmatic RCTs, and perspectives on the appropriate oversight of pragmatic RCTs. Interviews were coded inductively by two coders. Interim and final analyses were presented to the broader team for comment and discussion before the analytic framework was finalized. RESULTS: We conducted 45 interviews between April and September 2018. Interviewees represented a range of disciplines and jurisdictions as well as varying content expertise. Issues of importance in pragmatic RCTs were (1) identification of relevant risks from trial participation and determination of what constitutes minimal risk; (2) determining when alterations to traditional informed consent approaches are appropriate; (3) the distinction between research, quality improvement, and practice; (4) the potential for broader populations to be affected by the trial and what protections they might be owed; (5) the broader range of trial stakeholders in pragmatic RCTs, and determining their roles and responsibilities; and (6) determining what constitutes "usual care" and implications for trial reporting. CONCLUSIONS: Our findings suggest both the need to discuss familiar ethical topics in new ways and that there are new ethical issues in pragmatic RCTs that need greater attention. Addressing the highlighted issues and developing guidance will require multidisciplinary input, including patient and community members, within a broader and more comprehensive analysis that extends beyond consent and attends to the identified considerations relating to risk and stakeholder roles and responsibilities.
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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 | 0.413 | 0.227 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".