Efficacy and moderators of mindfulness-based cognitive therapy (MBCT) in ‘Difficult to Treat’ depression: protocol for a systematic review and individual participant data meta-analysis of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: About 30% of depressed patients suffer from a protracted course in which the disorder continues to cause significant burden despite treatment efforts. While originally developed for relapse prevention, mindfulness-based cognitive therapy (MBCT) has increasingly been investigated in depressed patients with such 'difficult-to-treat' courses. This is a protocol for an individual participant data (IPD) meta-analysis aiming to determine efficacy and potential moderators of MBCT treatment effects in this group based on evidence from randomised controlled trials. METHODS AND ANALYSIS: Systematic searches in PubMed, Web of Science, Scopus, PsycINFO, EMBASE and the Cochrane Controlled Trials Register for randomised controlled trials were completed on 17 June 2024. Authors of identified studies have contributed IPD, and data extractions have been completed. An update search will be conducted immediately before the start of data analyses. We will investigate the following outcomes: (a) self-reported and observer-reported severity of depression symptomatology, (b) remission and (c) clinically meaningful improvement and deterioration. One-stage and two-stage IPD-MA will be conducted with one-stage models using the observed IPD from all studies simultaneously as the primary approach. One-stage IPD models will include stratified study intercepts and error terms as well as random effects to capture between-study heterogeneity. Moderator analyses will test treatment-covariate interactions for both individual patient-level and study-level characteristics. ETHICS AND DISSEMINATION: The results will inform understanding of the use of MBCT in patients with current 'difficult-to-treat' depression and will contribute to arguments in favour of or against implementing MBCT as a treatment for this group. They will be published in a peer-reviewed journal and made available to stakeholders in accessible formats. No local ethical review was necessary following consultation with the Ethics and Governance Board of the University of Surrey. Guidance on patient data storage and management will be adhered to throughout. PROSPERO REGISTRATION NUMBER: CRD42022332039.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.123 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.026 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".