Cognitive -behavioral therapy for managing depressive and anxiety symptoms after stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Post-stroke anxiety and depression can be disabling and result in impaired recovery. Cognitive-behavioral therapy (CBT) has been demonstrated to be effective for anxiety and depression; however, determining its efficacy among those with stroke is warranted. Our objectives to evaluate CBT for anxiety and depression post-stroke .Methods This review was registered with PROSPERO (REG# CRD42020186324). Medline, PsycInfo, and EMBR Cochrane were used to locate studies published before May 2020, using keywords such as stroke and CBT. A study was included if: (1) interventions were CBT-based, targeting anxiety and/or depression; (2) participants experienced a stroke at least 3 months previous; (3) participants were at least 18 years old. Standardized mean differences ± standard errors and 95% confidence intervals were calculated, and heterogeneity was determined. The Cochrane Risk of Bias tool was used.Results The search yielded 563 articles, of which 10 (N = 672) were included;6 were randomized controlled trials. Primary reasons for exclusion included: (1) wrong population (2) insufficient data provided for a meta-analysis; (3) wrongoutcomes. CBT showed large effects on reducing overall anxiety (SMD ± SE: 1.01 ± 0.32, p < .001) and depression (SMD ± SE: 0.95 ± 0.22, p < .000) symptoms at the end of the studies. CBT moderately maintained anxiety (SDM ± SE: 0.779 ± 0.348, p ˂.025) and depression (SDM ± SE: 0.622 ± 0.285, p ˂ .029) scores after 3-months. Limitations included small sample size, limited comparators, and lack of follow-up data.Conclusion The results of this meta-analysis provide substantial evidence for the use of CBTto manage post-stroke anxiety and depression.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".