Back

Effect of non-drug Intervention on Cognitive Function and Blood Sugar Control of Type 2 diabetes Patients with Mild Cognitive Impairment: A meta-analysis Protocol

Wenhao, S.; Yanru, W.; Hairong, J.; Luo, Y.; Xueling, L.; Jiaqi, Z.; Zhaoyang, W.; Pepertual, T.

2024-09-22 public and global health
10.1101/2024.09.19.24314026 medRxiv
Show abstract

This study aims to assess non-drug therapies safety and effectiveness on cognitive function and blood glucose control of type 2 diabetes with mild cognitive impairment (T2DM-MCI) patients in randomized controlled trials by meta-analysis, providing constructive evidence for non-drug treatment decision-making. The system review plan will strictly follow the Systematic Review and Meta-Analysis Protocols entry for reporting. PubMed, EMBASE, Cochrane Library, CINAHL, Web of Science, CNKI, WANGFANG Database, and SinoMed will be systematically searched with Chinese and English language restrictions, and all randomized controlled trials comparing non-drug treatment with usual care or no intervention or placebo to study cognitive impairment in T2DM-MCI patients will be included. We will also manually search for cited literature. Our primary outcomes are cognitive function and blood sugar control. The risk of bias will be assessed for all studies using the Cochrane risk-of-bias tool (RoB 2) for Systematic Review of Intervention-version 5.1.0. Where possible, meta-analysis using random-effects models will be performed, otherwise, a qualitative summary will be provided. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) guidelines. This meta-analysis compares the efficacy of non-drug interventions for mild cognitive impairment in type 2 diabetes mellitus. It will provide reliable evidence for patients, clinicians, and researchers. The methodological protocol was in registered in the PROSPERO (CRD 42024496248).

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.