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Record W4309458358 · doi:10.3390/ph15111424

Adjuvant Chinese Medicine for the Treatment of Type 2 Diabetes Mellitus Combined with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis of a Randomised Controlled Trial

2022· review· en· W4309458358 on OpenAlexaboutno aff
Changxing Liu, Xinyi Guo

Bibliographic record

VenuePharmaceuticals · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineMeta-analysisAdverse effectMEDLINERandomized controlled trialIncidence (geometry)Montreal Cognitive AssessmentMini–Mental State ExaminationTraditional Chinese medicineDiabetes mellitusPopulationPhysical therapyCognitive impairmentAlternative medicineDiseasePathologyEndocrinology

Abstract

fetched live from OpenAlex

Mild cognitive impairment has a high prevalence in the type 2 diabetic population. Adjuvant therapy with Chinese herbal medicine can effectively improve the clinical symptoms of patients with T2DM combined with MCI. The aim of this study was to systematically evaluate the efficacy and safety of Chinese herbal adjunctive therapy in the treatment of diabetes mellitus combined with cognitive impairment. Information was analysed using the China Knowledge Network, Vip Database, Wanfang Database, China Biomedical Literature Database, PubMed, EMbase, Web of Science, and MedLine Database. The total clinical efficiency, blood glucose, blood lipids, Simple Mental-State Examination Scale (MMSE), Montreal Cognitive Assessment Scale (MoCA), Traditional Chinese Medicine Symptom Score (TCMSS), and incidence of adverse reactions were recorded. The methodological quality of the included studies was evaluated using the application of the Cochrane Collaboration Network Risk Bias Assessment Tool, and meta-analysis was performed using RevMan 5.4 software. Adjuvant treatment with Chinese herbal medicine was effective in improving the clinical outcomes (OR = 5.33, 95% CI (3.62, 7.84), p < 0.00001) and cognitive function by comparing with the control group: MMSE (MD = 1.56, 95% CI (1.29, 1.84), p < 0.00001) and MoCA (MD = 2.77, 95% CI (1.81, 3.73), p < 0.0001); lowered blood glucose: fasting blood glucose (FBG) (MD = −0.27, 95% CI (−0.42, −0.12), p = 0.0006), 2 hPG (MD = −0.28, 95% CI (−0.45, −0.10), p = 0.002), and glycated haemoglobin (HbA1c) (MD = −0.26, 95% CI (−0.39, −0.14), p < 0.001); and improved lipids: total cholesterol (TC) (MD = −0.51, 95% CI (−0.82, −0.21), p = 0.001), triglycerides (TGs) (MD = −0.46, 95% CI −0.46, 95% CI (−0.80, −0.11), p = 0.009), low-density lipoprotein (LDL-C) (MD = −0.28, 95% CI (−0.55, −0.02), p = 0.04), high-density lipoprotein (HDL-C) (MD = 0.17, 95% CI (0.07, 0.28), p = 0.001), reduced TCMSS (MD = −1.84, 95% CI (−2.58, −1.10), p < 0.0001), and incidence of adverse events (OR = 0.46, 95% CI (0.24, 0.88), p = 0.02). In conclusion, through the available evidence, herbal adjuvant therapy for T2DM combined with MCI was observed to be effective and did not significantly increase the adverse effects. Due to the limitation of the number and quality of the included studies, the abovementioned results need to be validated by further high-quality studies.

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)0.008
metaresearch head score (Gemma)0.016
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 score0.019
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.029
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
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 head0.209
GPT teacher head0.432
Teacher spread0.223 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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