Harnessing the power of placebos in movement disorders: Insights from Parkinson's disease in clinical research and practice
Bibliographic record
Abstract
The placebo effect alongside the detrimental nocebo and lessebo effects associated with the use of placebos are well-recognized phenomena. Research conducted in the last two decades has demonstrated that the placebo effect is a measurable health benefit with well-known neurobiological correlates. In this review, we describe the current knowledge about how to best make use of the placebo effect to improve clinical research and optimize clinical care. We will cover four key topics: (1) setting the stage: historical perspectives on the use of placebos; (2) optimizing the use of placebos in clinical trials; (3) use of placebos in clinical practice; and (4) how can we minimize the ethical risks of using placebos? The adoption of the placebo-controlled trial as a standard in the therapeutic development reduced the placebo effect to background noise in a therapeutic signal. Strategies to minimize the placebo effect in clinical trials have been used to obtain positive efficacy results. In more recent years, new study paradigms encouraged a change in the views on placebos and the placebo effect. Alternative study designs and the development of biomarkers for a placebo response permit a better understanding of the contribution of the placebo effect to the results of interventional studies, without minimizing it. In clinical practice, optimizing the placebo effect (and reducing nocebo effects) has the potential to improve care for our patients, but raises ethical challenges. An enhanced patient-physician relationship would be central to any evidence-based intervention used in clinical care to heighten the placebo effect. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.057 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".