A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.

This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform nursing practice and implementation strategies. Studies published between 2019 and 2024 were systematically identified through searches in Scopus, PubMed, Medline, ScienceDirect, CINAHL Plus with Full Text, Clinical Key, Ovid, EMBASE, and Web of Science. The review adhered to PRISMA guidelines, with rigorous inclusion and exclusion criteria applied.

Factor Impacting Quality of Life Among Sepsis Survivors During and After Hospitalization: A Systematic Review of Current Empirical Evidence.

Background: There remains a gap in understanding post-sepsis outcomes, particularly regarding the factors that influence the quality of life (QOL) among sepsis survivors during and after hospitalization.

Objective: To determine factors impacting QOL among sepsis survivors during and after hospitalization based on the evaluation and synthesis of current evidence.

Methods: This review encompassed studies published from January 2020 to December 2024, sourced from Scopus, PubMed, Medline, ScienceDirect, CINAHL Plus with Full Text, and Web of Science.