ArticleBMC women's health2025
Predicting the risk of high-grade precancerous cervical lesions based on high-risk HPV typing in Changsha China.
Article in BMC women's health, 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.
- Epidemiological characteristics and genotype distribution of low-risk human papillomavirus infection among women in Jingzhou, Central China: a cross-sectional study.BMC infectious diseases · 2026Article
- Clinical value of first-morning urine exfoliated cell HPV detection in the diagnosis of high-grade cervical lesions.Frontiers in oncology · 2026Article
- Epidemiological analysis of human papillomavirus infection in senior women: a hospital-based retrospective study.BMC infectious diseases · 2025Article
- Trends in HPV-positive cervical cancer prevalence: a retrospective study from 2013 to 2020.Virology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundPersistent infection with high-risk human papillomavirus (HPV) is a significant risk factor for cervical cancer. HPV typing and cytology are conducted in women of appropriate age to assess the risk of cervical lesions and to guide the need for further diagnostic procedures such as colposcopy, cervical biopsy, or treatment. This article explores methods to predict the risks of high-grade precancerous cervical lesions based on high-risk HPV typing.
methodsWe conducted a retrospective analysis of HPV typing data from 158,565 women, including 19,707 who underwent ThinPrep cytologic testing (TCT), 7,539 who had colposcopy examinations, and 4,762 who had biopsies. We evaluated the sensitivity, specificity, and risk parameters of high-grade lesions associated with high-risk HPV types.
results(1) The overall prevalence of HPV infection was 17.89%, with the most prevalent types being HPV52 (4.44%), HPV58 (2.10%), HPV53 (1.96%), HPV81 (1.85%), HPV42 (1.75%), and HPV16 (1.44%). (2) The sensitivity and specificity of detecting high-grade lesions in TCT, colposcopy, and biopsy, based on high-risk HPV typing, demonstrated a strong linear correlation with the infection rate of each type. (3) HPV16 was confirmed to have a higher risk of CIN2 + in biopsies using a self-defined risk parameter. (4) The top five HPV types with the highest PPVs and pathogenicity risks in biopsies were HPV45, HPV16, HPV58, HPV33, and HPV18.
conclusionIn Changsha, China, HPV52, HPV58, and HPV53 were the most prevalent and contributed significantly to high-grade lesions. After adjusting for infection rates, a self-defined risk parameter was proposed as a measure of the intrinsic risks of high-grade lesions associated with high-risk HPV types. Focused monitoring of prevalent high-risk HPV types such as HPV45, HPV16, HPV58, HPV33, and HPV18, which show the highest pathogenicity risks, is recommended in our region.
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.