Evidence map›Paper›PMID 37805467›Full record

ArticleBMC infectious diseases2023

Epidemiologic characteristics of high-risk HPV and the correlation between multiple infections and cervical lesions.

Qinli Luo, Xianghua Zeng, Hanyi Luo, Ling Pan, Ying Huang, Haiyan Zhang, Na Han

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
10.3field-weighted citation impact, top 1% of its field
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

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.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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 at 1 institution in 1 country.

Qinli LuoChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China. m13668488467@163.com.
Xianghua ZengChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China.
Hanyi LuoChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China.
Ling PanChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China.
Ying HuangChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China.
Haiyan ZhangChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China. 1034759119@qq.com.
Na HanChongqing Cancer Multi-Omics Big Data Application Engineering Research Center,, Chongqing University Cancer Hospital, 181 Hanyu Road, Shapingba District, Chongqing, 400030, China. 1460964479@qq.com.
Chongqing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to determine the prevalence of high-risk human papillomavirus (HR-HPV) and the correlation between multiple infections and cervical lesions.

methodsThe current study involved population-based sample of 20,059 women who underwent cervical screening for 15 HR-HPV genotypes with ThinPrep cytologic test (TCT) results. The correlation between multiple HPV genotype infections and cervical lesions was also determined. The odds ratios (ORs) were calculated to assess co-infection patterns for each genotype with 15 other genotypes and the additive statistical interactions were evaluated.

resultsThere was a bimodal pattern among multiple HPV infections, with a peak in the younger group and a second peak in the elderly group. Indeed, most multiple HPV genotypes exhibited a bimodal pattern. The most common HPV type in patients with high-grade squamous intraepithelial lesions (HSILs) was HPV-16, followed by HPV-52, HPV-58, and HPV-33. The most frequent HPV type in patients with cervical cancer was HPV-16, followed by HPV-58 and HPV-33. Women with multiple infections were at a increased risk of low-grade squamous intraepithelial lesions [LSIL] (OR = 2.01; 95% CI 1.38-2.93) and HSIL (OR 2.28; 95% CI 1.36-3.81) when compared to women with single infections. patients with cervical cancer had the higher percentage of multiple HPV infections. Based on the data herein, we suggest that HPV-33 and HPV-58 may also be high-risk HPV types worthy of increased surveillance and follow-up.

conclusionOur findings suggested that the association between multiple HPV infections and HSIL and LSIL are stronger compared to single HPV infections. There may be some specific combinations that synergistically affected the risk of HSIL and LSIL.

Indexed as

Papillomavirus InfectionsUterine Cervical DysplasiaUterine Cervical NeoplasmsAgedAlphapapillomavirusEarly Detection of CancerFemaleGenotypeHuman papillomavirus 16Human Papillomavirus VirusesHumansPapillomaviridaeGenotypeHuman papillomavirus (HPV)Multiple infectionsThinPrep cytological test

Identifiers

PMID37805467
PMCPMC10560423
OpenAlexW4387424075

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.