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Record W2889768078 · doi:10.1155/2018/7541406

Chinese Medicinal Herbs in the Treatment of Diabetic Cognitive Impairment: A Systematic Review and Meta‐Analysis

2018· review· en· W2889768078 on OpenAlexaboutno aff
Bin Yan, Jingbo Wang, Zhigang Xue, Guoqing Tian

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2018
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentMeta-analysisMedicineMedicinal herbsTraditional medicineCognitionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.023
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.228
GPT teacher head0.428
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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