Protocol for a systematic review and meta-analysis of the placebo response in treatment-resistant depression: comparison of multiple treatment modalities
Bibliographic record
Abstract
INTRODUCTION: The high placebo response in depression treatment trials is a major contributing factor for randomised control trial failure to establish efficacy of novel or repurposed treatments in treatment-resistant depression (TRD) and major depressive disorder in general. Though there have been a number of meta-analyses and primary research studies evaluating the placebo response in non-TRD, placebo response in TRD is poorly understood. It is important to understand the placebo response of TRD as treatments are only moderately effective and up to 1/3 of patients will experience TRD. METHODS AND ANALYSIS: We will conduct a search of electronic databases (MEDLINE and PsychINFO) from inception to 24th January 2020 including randomised, placebo-controlled trials of pharmacological, somatic and psychological interventions for adults with TRD. TRD will be defined as a failure to respond to at least two interventions of adequate dose or duration. We will also search reference lists from review articles. We will perform several meta-analyses to quantify the placebo response for each treatment modality. Regression analysis will explore potential contributing demographic and clinical variables to the placebo response. We will use Cochrane risk of bias tool. ETHICS AND DISSEMINATION: There is no research ethics board approval required. The dissemination plan is to publish results in a peer-reviewed academic journal. PROSPERO REGISTRATION NUMBER: 190 465.
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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 | 0.087 | 0.136 |
| Meta-epidemiology (narrow) | 0.011 | 0.007 |
| Meta-epidemiology (broad) | 0.036 | 0.036 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.085 | 0.013 |
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".