Butylphthalide combined with donepezil for the treatment of vascular dementia: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate the efficacy and safety of butylphthalide combined with donepezil versus butylphthalide monotherapy for the treatment of vascular dementia. METHODS: Randomized controlled trials were searched in electronic databases, including PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, Chinese Science and Technology Periodical Database (VIP), Wan Fang, and China Biology Medicine from inception to 29 November 2022. Two reviewers independently screened the papers and extracted data from the included studies. The data were processed using RevMan5.4 statistical software. RESULTS: Nine randomized controlled trials (n = 1024) were included in this meta-analysis. Regarding the primary outcomes, compared with butylphthalide monotherapy, combined butylphthalide and donepezil treatment exhibited significantly greater total clinical efficacy (relative risk = 1.24, 95% confidence interval [1.17, 1.31]) and did not increase the adverse event rate (relative risk = 1.39, 95% confidence interval [0.91, 2.14]). Regarding the secondary outcomes, the meta-analysis results for the Mini-Mental State Examination, abilities of daily living, and Montreal Cognitive Assessment scores and the interleukin-6, tumor necrosis factor-α, and superoxide dismutase blood levels all supported combined butylphthalide and donepezil treatment. CONCLUSION: Butylphthalide combined with donepezil may be a better treatment strategy than donepezil alone for the treatment of vascular dementia in clinical practice.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".