The ethics of placebo in clinical psychopharmacology: the urgent need for consistent regulation
Bibliographic record
Abstract
The ethics of research on humans is a topic that elicits much debate. One of the current hot topics is the ethics of the use of placebo. Strongly held beliefs in the research community diverge widely. The principle of clinical equipoise1 requires a genuine uncertainty on the part of the expert medical community about the comparative therapeutic merits of each arm of a clinical trial. Acceptance of clinical equipoise necessarily implies that the use of placebo is unacceptable in any situation where there is an effective treatment.2,3 Some have argued that preventing the use of placebo means that more patients are exposed to experimental medications that are potentially without efficacy and may have serious adverse effects (e.g., the Canadian College of Neuropsychopharmacology position paper on ethics of placebo4). Furthermore, examples can be found of situations in which the evaluation of potentially beneficial treatments is problematic if strict adherence to the principle of equipoise is required. For example, under clinical trial conditions, in patients with major depressive disorder, the difference between the effects of a standard treatment and placebo is small. In the absence of a placebo arm, a new treatment could appear to be of similar effectiveness to standard treatment, yet actually be no better than a placebo. Therefore, placebo should be permitted in clearly defined circumstances, even if standard treatments exist, as long as steps are taken to minimize the risk to patients and as long as the patients understand the nature of the risks they are taking.
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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 | 0.299 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.083 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.033 | 0.052 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".