Evidence-Based Review of Randomized Controlled Trials of Interventions for Mental Health Management Post-Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To present an evidence-based review of randomized controlled trials (RCTs) evaluating interventions for mental health post-moderate to severe traumatic brain injury (post-MSTBI), as part of an extensive database that has been conceptualized as a living systematic review. METHODS: Systematic searches were conducted for RCTs published in the English language in MEDLINE, PubMed, Scopus, CINAHL, EMBASE, and PsycINFO, up to and including December 2022, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The methodological quality of RCTs was assessed using the Physiotherapy Evidence Database scale, and the level of evidence was assigned using a modified Sackett scale. RESULTS: Eighty-seven RCTs examining mental health interventions and outcome measures post-MSTBI were included. These studies collectively enrolled 6471 participants. A total of 41 RCTs (47.1%) were conducted in the United States and 56 studies (64.4%) were published after 2010. A total of 62 RCTs (71.3%) examined nonpharmacological interventions and 25 RCTs (28.7%) examined pharmacological interventions. Effective pharmacological treatments included desipramine and cerebrolysin; methylphenidate and rivastigmine showed conflicting evidence. Cognitive behavioral therapy (CBT) was found to be effective for hopelessness, stress, and anxiety, compared to usual care; however, it may be as effective as supportive psychotherapy for depression. CBT combined with motivational interviewing may be as effective as CBT combined with nondirective counseling for depression, stress, and anxiety. Acceptance and commitment therapy was effective for anxiety, stress, and depression. Tai Chi, dance, and walking appeared to be effective for depression and stress, while other nonpharmacological treatments such as peer mentoring showed limited effectiveness. CONCLUSION: This evidence-based review provides a comprehensive overview of the research landscape of RCTs addressing mental health post-MSTBI. The findings from these RCTs may be valuable for health care professionals, researchers, and policymakers involved in the field of mental health and neurorehabilitation.
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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 | 0.034 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".