A systematic review on the effectiveness of dialectical behavior therapy for improving mood symptoms in bipolar disorders
Bibliographic record
Abstract
BACKGROUND: Evidence-based psychotherapies available to treat patients with bipolar disorders (BD) are limited. Dialectical behavior therapy (DBT) may target several common symptoms of BD. We conducted a systematic review on the efficacy of DBT for mood symptoms in patients with BD. The systematic search used key words related to DBT and BD in Medline, Embase, PsycInfo, CINAHL, and Cochrane Library databases from 1980 to April 1st, 2022. We included studies that enrolled patients with a BD I or II diagnosis (DSM or ICD), age 12 and older who received a DBT-based intervention. Studies reviewed were clinical trials including observational studies that reported at least one outcome related to BD mood symptoms or severity. We did not exclude based upon psychiatric or physical co-morbidity. RESULTS: We screened 848 abstracts and reviewed 28 full texts; 10 publications with 11 studies met our pre-determined eligibility criteria. All but one were feasibility pilot studies and most included participants in all mood states except for mania. The studies provided preliminary evidence suggesting these interventions may be effective for improving several core symptoms of BD. Overall, all the studies consistently supported that DBT-based interventions are feasible and acceptable for patients with BD. CONCLUSION: DBT may be an effective treatment for BD; however, the confidence in this conclusion is limited by the small sample sizes, heterogeneity, and high risk of bias in all published trials. Larger well-designed RCTs are now required to establish the effectiveness of DBT in BD.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".