Cognitive behavioral therapy for managing depressive and anxiety symptoms after brain injury: a meta-analysis
Bibliographic record
Abstract
Background Individuals with traumatic brain injury (TBI) are at increased risk of depression and anxiety, leading to impaired recovery. While cognitive-behavioral therapy (CBT) addresses anxiety and depression maintenance factors, its efficacy among those with TBI has not been clearly demonstrated. This review aims to bridge this gap in the literature.Methods Several databases, including Medline, PsycInfo and EMBASE, were used to identify studies published between 1990 and 2021. Studies were included if: (1) trials were randomized controlled trials (RCT) involving CBT-based intervention targeting anxiety and/or depression; (2) participants experienced brain injury at least 3-months previous; (3) participants were ≥18 years old. An SMD ± SE, 95% CI and heterogeneity were calculated for each outcome.Results Thirteen RCTs were included in this meta-analysis. The pooled-sample analyses suggest that CBT interventions had small immediate post-treatment effects on reducing depression (SMD ± SE: 0.391 ± 0.126, p < 0.005) and anxiety (SMD ± SE: 0.247 ± 0.081, p < 0.005). Effects were sustained at a 3-months follow-up for depression. A larger effect for CBT was seen when compared with supportive therapy than control. Another sub-analysis found that individualized CBT resulted in a slightly higher effect compared to group-based CBT.Conclusion This meta-analysis provides substantial evidence for CBT in managing anxiety and depression post-TBI.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".