ArticleParasitology research2025
Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning.
Article in Parasitology research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Empirical therapy for hepatic lesions in suspected clonorchiasis: a comparative efficacy analysis of Albendazole and Praziquantel.Frontiers in pharmacology · 2026Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Disentangling the transmission ofOne health (Amsterdam, Netherlands) · 2025Article
- Current knowledge on the prevalence and detection techniques ofFrontiers in veterinary science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study aimed to evaluate the prevalence and identify the risk factors of C. sinensis infection in Longsheng County, a mountainous area in northern Guangxi. In 2023, a comprehensive study was conducted in Longsheng County, utilizing longstanding inhabitants as study participants. Questionnaires were employed to gather data on fish consumption, awareness of C. sinensis, and residential coordinates, while fecal examinations were utilized to identify C. sinensis infection. Important risk factors for the C. sinensis infection were identified through the development of individual infection risk models using automated machine learning techniques. A total of 740 fecal samples were collected, revealing an overall C. sinensis infection rate of 69.59%. The gradient boosting machine (GBM) was the most accurate predictor with an area under the precision-recall Curve (AUPRC) of 0.997. The model identified years of raw fresh fish consumption, frequency of raw fresh fish consumption, elevation, and water distance as the top four predictors of C. sinensis infection risk. In conclusion, our study has revealed a high infection rate of C. sinensis in the mountainous areas of Longsheng County, with adults, men, and farmers particularly susceptible to both high incidence and infection severity. We developed a high-performance predictive model for individual C. sinensis infection within the county, identifying the key risk factors for local infections. These findings offer valuable guidance for the control and prevention of clonorchiasis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.