Research Governance Lessons from the National Placebo Initiative
Bibliographic record
Abstract
1. Introduction For at least the last two decades, Canada has been an international leader in research ethics. Canadian scholars have written seminal articles that now fill standard texts in the field. For example, in the authoritative collection Ethical and Regulatory Aspects of Clinical Research, fully eighteen of 86 articles included in the volume were authored in Canada. (1) In the realm of research ethics policy, Canada's contributions are also many. The Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (2) (TCPS), introduced a decade ago and currently under revision, is widely admired for its scope and substance. More recently the Canadian Institutes of Health Research Guidelines for Research Involving Aboriginal People (3) attracted considerable interest as a potential roadmap for effective research partnerships between aboriginal communities and researchers. The National Placebo Initiative (NPI) was established in 2002 with a mandate to broker consistent guidance on the use of placebos in research in Canada. Although the recommendations set forth in its Final Report (2004) (4) have the potential to renew Canada's role as an international leader in research ethics, they have yet to be acted upon by the Canadian Institutes of Health Research and Health Canada. In this paper we discuss the history of the placebo question in Canada, describe the recommendations made by the NPI, and attempt to identify some of the reasons for their lack of uptake. 2. Historical Background Since the 1980s, Canada has been at the center of scholarly work on the ethics of randomized controlled trials (RCT). (5) Early work focused on the ethics of randomization. It is widely acknowledged that the physician owes her patient a duty of care that requires the physician to act and advise in accordance with the patient's best medical interests. In a RCT, the participant is allocated by chance to an experimental or control How, critics asked, could offering a patient enrollment in a RCT ever be consistent with the physician's duty of care? Benjamin Freedman provided the most widely accepted answer to this question with his concept of clinical equipoise. (6) According to Freedman, a physician may legitimately offer a patient RCT enrollment provided that each of the treatment arms to which she may be allocated is consistent with competent medical care. In other words, equipoise requires that at the start of a RCT [t]here exist ... an honest, professional disagreement among [the community of] expert clinicians about the preferred treatment. (7) Generally, a placebo control is appropriate when there is no proven treatment for the study condition. However, once proven treatment exists, an active control (i.e., standard treatment) ought to be used. Not only does this ensure that patients enrolled in a trial will not go untreated when a proven therapy is available, but comparison to an active control provides valuable information on comparative efficacy. If the new drug eventually receives regulatory approval, information on comparative efficacy is essential for informed decision making by policy makers, clinicians, and patients. Clinical equipoise has clear implications for use of placebo controls in RCTs. In a 1990 article in the journal IRB, Freedman laid out circumstances in which a placebo control may be used consistently with equipoise, namely when: 1. there is no standard treatment; 2. standard treatment is no better than placebo; 3. standard treatment is placebo; 4. the net therapeutic advantage of standard treatment has been called into question by new evidence; or, 5. effective treatment exists but is not available due to cost or short supply. (8) Two further circumstances in which a placebo control is licit are entailed by Freedman's list. First, a placebo control may be used in a population of patients who don't respond to standard treatment, provided no effective second-line treatment exists (there is no standard treatment). …
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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 | 0.111 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.060 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".