Evidence map›Paper›PMID 40177168›Full record

ArticleInfection and drug resistance2025

Etiological Factors and Development of a Predictive Model for Urinary Tract Infections in Cervical Cancer Patients Undergoing Intensity-Modulated Radiotherapy.

Ying Yao, Lijun Tao, Li Ma, Yuhan Fan, Dongju Zheng, Jian Wang, Wen Chen

Abstract read
In one paragraph

Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ying Yao *Department of Pharmacy, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia, People's Republic of China.
Lijun Tao *Department of Pharmacy, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia, People's Republic of China.
Li MaDepartment of Radiotherapy, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, People's Republic of China.
Yuhan FanDepartment of Preparation Center, Ningxia Medical University General Hospital, Yinchuan, Ningxia, People's Republic of China.
Dongju ZhengDepartment of Radiotherapy, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, People's Republic of China.
Jian WangDepartment of Pharmacy, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia, People's Republic of China.
Wen ChenDepartment of Pharmacy, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the distribution characteristics of pathogenic bacteria causing urinary tract infections in cervical cancer patients undergoing intensity-modulated radiotherapy (IMRT). Furthermore, to explore the risk factors and predictive factors associated with urinary tract infections, and to establish a personalized risk prediction model. Methods: A retrospective study analyzed 160 cervical cancer patients undergoing intensity-modulated radiotherapy at the People's Hospital of Ningxia Hui Autonomous Region from 2020 to 2023. The clinical characteristics of the participants were collected, and in combination with microbiological culture results, the distribution and drug resistance of pathogens causing urinary tract infections were analyzed. Using logistic regression and multivariable logistic analysis, we established a predictive model that includes clinical variables. Results: Urinary specimens were collected and analyzed from 52 patients with urinary tract infections. The incidence of urinary tract infections in cervical cancer patients after radiotherapy in this study was approximately 32.5%, with the predominant pathogens identified as Conclusion:

Indexed as

cervical cancerIMRTmodelnomogramurinary tract infections

Identifiers

PMID40177168
PMCPMC11963793

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.