Chinese Medicinal Herbs in the Treatment of Diabetic Cognitive Impairment: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic cognitive impairment (DCI), a serious complication of diabetes mellitus (DM), is gaining more acceptance and attention. The learning and memory function of diabetics always decreases. Traditional Chinese medicine (TCM) has been demonstrated to be effective in treating the symptoms in China, and thereinto Chinese medicinal herbs (CMH) are the most widely used. The objective of the present study was to review and analyze the existing data about reducing the symptoms in CMH treatment for DCI. METHODS: Electronic literature databases (PubMed, EMBASE, CNKI, SinoMed, and Wan fang) were searched for randomized controlled trials conducted in China, comparing CMH with western medicines in the treatment of DCI, up to April 1, 2018. We applied standard meta-analytic techniques to analyze data from papers that reached acceptable criteria. RESULT: Nine randomized controlled trials (n = 576) on CMH were included. We found moderate evidence that CMH used alone or in combination with western medicines was more effective than western medicines alone in reliving the symptoms for DCI (total effective rate, odds radio (OR) = 4.64 (2.60, 8.29), and 95% confidence interval, P<0.00001). Besides, CMH along or in combination with western medicines showed more beneficial effects on Montreal Cognitive Assessment (MoCA) scale (mean difference (MD) = 1.31(0.75, 1.87), P<0.00001), Mini-Mental State Examination (MMSE) scale (MD = 2.07 (0.86, 3.28), P<0.00001, and TCM symptom score (TCMSS) (MD = -4.89 (-8.44, -1.34), P = 0.007). Most of the included studies showed that there was not a significant difference in the adverse events. CONCLUSIONS: These findings demonstrated that CMH used alone or in combination with western medicines were apparently better than western medicines alone in the treatment of DCI. Because of the poor quality of the studies that were available for the present meta-analysis, further researches are still needed to support these early findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".