Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.

Background: Colorectal cancer (CRC) is a highly aggressive and extensive malignancy. Although long noncoding RNAs (lncRNAs) are often used as diagnostic biomarkers, their diagnostic effectiveness in CRC remains uncertain.

Methods: From January 1, 2015, to April 1, 2024, we conducted a comprehensive search of Embase, China National Knowledge Infrastructure (CNKI), Wanfang, PubMed, Cochrane Library, and Web of Science (WoS).

Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.

While machine learning (ML) approaches are commonly utilized in medical diagnostics, the accuracy of these methods in identifying psoriatic arthritis (PsA) remains uncertain. To evaluate the accuracy of ML approaches in the medical diagnosis of PsA. As a result, we thoroughly searched PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the Chinese National Knowledge Infrastructure (CNKI) between their inception and October 1, 2024.

Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.

Although machine learning is frequently used in medicine for predictive purposes, its accuracy in diabetes-related amputation (DRA) remains unclear. From establishing the database until December 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the China National Knowledge Index (CNKI). The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and Fagan plot analysis were used to assess the overall test performance of machine learning.