The clinical efficacy of psychological interventions for bipolar depression: a systematic review and individual patient data (IPD) meta-analysis
Bibliographic record
Abstract
Unlike conventional meta-analyses, individual patient data (IPD) meta-analysis assesses moderator variables at the level of each participant, which generates more precise and biased estimates. The objective of this study was to investigate whether psychological therapy reduces depression symptoms in people with Bipolar I and II disorders and examine whether baseline depression has a moderating effect on treatment outcomes. Through the use of several electronic databases, a systematic search was conducted. Eligible studies were randomized controlled trials evaluating a psychological intervention for adults diagnosed with Bipolar I or II disorder. Titles and abstracts were screened, followed by full texts. The authors of the included studies were asked to provide IPD from their trials. A multilevel model approach was used to analyze the data. From the 7552 studies found by our searches, six studies with 668 study participants were eligible. Intervention significantly reduced depression scores. There was a significant association between baseline depression and post treatment depression scores. There was no statistically significant interaction between condition allocation and baseline depression score. When IPD from the two most comparable studies were analyzed, CBT had reduced depression scores relative to the comparator condition. The study included patient data from only six studies which were heterogeneous in terms of intervention type, outcome measure, and comparators. Overall, the psychological interventions tested significantly reduced bipolar depression scores. There was no evidence of moderation by baseline depression scores.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".