Placebo effects in mental health disorders: protocol for an umbrella review
Bibliographic record
Abstract
INTRODUCTION: Given the high prevalence of mental health disorders and their significant socioeconomic burden, there is a need to develop improved treatments, and to evaluate them through placebo-controlled trials. However, the magnitude of the placebo response in randomised controlled trials to test medications may be substantial, affecting their interpretation. Therefore, improved understanding of the patient, trial and mental disorder factors that influence placebo responses would inform clinical trial design to better detect active treatment effects. There is a growing literature exploring the placebo response within specific mental health disorders, but no overarching synthesis of this research has been produced to date. We present a protocol for an umbrella review of systematic reviews and/or meta-analyses in which we aim to understand the effect size and potential predictors of placebo response within, and across, mental health disorders. METHODS AND ANALYSIS: We will systematically search databases (Medline, PsycINFO, EMBASE+EMBASE Classic, Web of Knowledge) for systematic reviews and/or meta-analyses that report placebo effect size in clinical trials in patients with mental health disorders (initial search date 23 October 2022). Screening of abstracts and full texts will be done in pairs. We will extract data to qualitatively examine how placebo effect size varies across mental health disorders. We also plan to qualitatively summarise predictors of increased placebo response identified either quantitatively (eg, through meta-regression) or qualitatively. Risk of bias will be assessed using the AMSTAR-2 tool. We aim to not only summarise the current literature but also to identify gaps in knowledge and generate further hypotheses. ETHICS AND DISSEMINATION: We do not believe there are any specific ethical considerations relevant to this study. We will publish the results in a peer-reviewed journal.
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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 | 0.117 | 0.180 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.100 | 0.025 |
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".