Construction and validation of a risk prediction model for symptom cluster distress in brain tumor patients: a cross-sectional study

The model constructed in this study has a good prediction effect, which can provide a reference for medical staff to quickly identify the risk of SCD in brain tumor patients and take timely preventive management measures, thereby improving the effectiveness of symptom management.

Authors

Institutions

Publication Details

Journal
BMC Cancer
Published
2025-11-22
DOI
https://doi.org/10.1186/s12885-025-15281-8
Primary Topic
Cancer survivorship and care
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction and validation of a risk prediction model for symptom cluster distress in brain tumor patients: a cross-sectional study

Qing Xiang, Duan Yuyu, Zhou Zhihuan, Ying Li et al.
BMC Cancer
Cancer survivorship and care
article

Construction and validation of a risk prediction model for symptom cluster distress in brain tumor patients: a cross-sectional study

Qing Xiang, Duan Yuyu, Zhou Zhihuan, Ying Li, Zou Daili, Su Yangmei, Luo Huidan
article en

Abstract

The model constructed in this study has a good prediction effect, which can provide a reference for medical staff to quickly identify the risk of SCD in brain tumor patients and take timely preventive management measures, thereby improving the effectiveness of symptom management.

BMC CancerVol. 25(1)
Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN)
Sun Yat-sen University, Sun Yat-sen University Cancer Center
Openalex Percentile: Top 51%
Cancer survivorship and care
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.