Efficacy and acceptability of adjunctive psychological and pharmacological interventions for treatment-resistant depression: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Major depressive disorder (MDD) is a common debilitating illness worldwide. The vast majority of patients with MDD will not achieve remission with first-line treatment and despite the availability of different treatment modalities, at least one-third of patients experience treatment-resistant depression (TRD). There continues to be a paucity of research focused on treatment options for patients with TRD thus treatment decisions are largely based on patient and clinician preference as opposed to evidence-based practice. Herein we propose a systematic review and network meta-analysis (NMA) of available pharmacological and psychological augmentation treatments for TRD, to inform evidence-based management of TRD. METHODS AND ANALYSIS: We plan to conduct a search of electronic databases (MEDLINE and ISIWEB) of all dates from inception for randomised controlled trials of pharmacological and psychological augmentation interventions for adults with TRD. Articles for review will be included based upon consensus from two authors. Pharmaceutical companies will be contacted for access to any unpublished data. An NMA will compare the effectiveness pharmacological adjunctive agents for TRD using preanalysis/postanalysis, assuming consistency and transitivity. ETHICS AND DISSEMINATION: This project does not require research ethics board approval. The dissemination plan is to present findings at international scientific meetings and publishing results in a peer-reviewed academic journal. PROSPERO REGISTRATION NUMBER: CRD42019132588.
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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 | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".