Oral Chinese herbal medicine combined with donepezil for mild cognitive impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background This study aims to evaluate the add‐on effects of oral Chinese herbal medicine (CHM) for mild cognitive impairment (MCI), when used in addition to donepezil compared to donepezil alone. Methods Randomized controlled trials comparing these treatments across all types of MCI were identified from nine databases and three registers until August 2023. Outcome measures were Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and adverse events (AEs). Methodological quality was assessed using Cochrane risk‐of‐bias tool, and evidence certainty was evaluated using the GRADE method. Results Involving 1611 participants across 20 studies, meta‐analysis results indicate that oral CHM combined with donepezil significantly improved cognitive function in MCI patients compared to donepezil alone, as evidenced by MMSE (1.88 [1.52, 2.24], I 2 = 41%, 12 studies, 993 participants) and MoCA (MD: 2.01 [1.57, 2.44], I 2 = 52%, 11 studies, 854 participants). Eleven studies reported details of AEs, identifying gastrointestinal symptoms and insomnia as the most common symptoms. No significant difference in AEs frequency was found between the groups (RR: 0.91 [0.59, 1.39], I 2 = 4%, 11 studies, 808 participants). All 20 studies were evaluated as having “some concerns” regarding the overall risk of bias. The certainty of evidence for MMSE was “moderate” and “low” for MoCA. From frequently utilized herbs, two classical CHM formulae were identified: Kai xin san and Si wu decoction. The observed treatment effects of commonly used herbs may be exerted through multiple pharmacological mechanisms, including anti‐inflammatory, anti‐oxidative stress, anti‐apoptotic actions, promotion of neuronal survival and modulation of the cholinergic system. Conclusions The concurrent use of oral CHM and donepezil appears to be more effective than donepezil alone in improving the cognitive function of MCI, without leading to an increase in AEs. While recognizing concerns of overall methodological quality, this combined therapy should be considered as an alternative option for clinical practice.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".