Related Experiment Video
Updated: Jan 20, 2026

Severe Burn Injury in a Swine Model for Clinical Dressing Assessment
Published on: November 6, 2018
Clinical prognostic models for severe dengue: a systematic review protocol
Thang Dao Phuoc1, Long Khuong Quynh1, Linh Vien Dang Khanh1
1University of Medicine and Pharmacy, Ho Chi Minh City, 700000, Vietnam.
Abstract:
Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta - Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase, and Web of Science. Eligibility criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907.
Related Concept Videos
Review and Preview
Percentiles are a type of fractile that partition data into...
Review and Preview
Random and Systematic Errors
Systematic Sampling Method
Systematic sampling is one of the simplest methods...
Propagation of Uncertainty from Systematic Error
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

