Cognitive -behavioral therapy for managing depressive and anxiety symptoms after stroke: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it