Evidence map›Paper›PMID 38224340›Full record

ArticleeLife2024

Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy.

Silvia de Sanjosé, Rebecca B Perkins, Nicole Campos, Federica Inturrisi, Didem Egemen, Brian Befano, Ana Cecilia Rodriguez, Jose Jerónimo, Li C Cheung, Kanan Desai and 9 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06815939 (Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% 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.

NCT06815939 narecruitingnot on this mapstarted 2025, after this paper: background citation

Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer: Automated Visual Evaluation

TypeinterventionalSponsorDL AnalyticsRan2025 to 2026Enrolled10,000ConditionsHuman Papillomavirus (HPV), Cervical Intraepithelial Neoplasia, Uterine Cervical Neoplasms, Cervical CancersArmsHPV Test, EVA System, Visual Inspection with Acetic Acid (VIA), Automated Visual Evaluation (AVE), Image Capture with Mobile Phone
3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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  3. Automated acquisition of explainable knowledge from unannotated colposcopic images for predicting the natural course of CIN2.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 6 institutions in 2 countries.

Silvia de SanjoséDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.ORCID https://orcid.org/0000-0002-5909-676X
Rebecca B PerkinsUniversity Chobanian and Avedisian School of Medicine/Boston Medical Center, Boston, United States.
Nicole CamposCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, United States.
Federica InturrisiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.ORCID https://orcid.org/0000-0002-3661-0842
Didem EgemenDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.
Brian BefanoInformation Management Services Inc, Calverton, United States.
Ana Cecilia RodriguezDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.
Jose JerónimoDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.
Li C CheungDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.ORCID https://orcid.org/0000-0003-1625-4331
Kanan DesaiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.ORCID https://orcid.org/0000-0002-8992-5944
Paul HanDivision of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, United States.
Akiva P NovetskyWestchester Medical Center/New York Medical College, Valhalla, United States.
Abigail UkwuaniDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.
Jenna MarcusFeinberg School of Medicine at Northwestern University, Chicago, United States.
Syed Rakin AhmedAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States.ORCID https://orcid.org/0000-0002-1615-8633
Nicolas WentzensenDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.
Jayashree Kalpathy-CramerAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States.
Mark SchiffmanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States.ORCID https://orcid.org/0000-0002-4625-2508
PAVE Study Group
National Institutes of Health · USBoston Medical Center · USDartmouth College · USMassachusetts General Hospital · USUniversity of Washington · USWestchester Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The HPV-automated visual evaluation (PAVE) Study is an extensive, multinational initiative designed to advance cervical cancer prevention in resource-constrained regions. Cervical cancer disproportionally affects regions with limited access to preventive measures. PAVE aims to assess a novel screening-triage-treatment strategy integrating self-sampled HPV testing, deep-learning-based automated visual evaluation (AVE), and targeted therapies. Methods: Phase 1 efficacy involves screening up to 100,000 women aged 25-49 across nine countries, using self-collected vaginal samples for hierarchical HPV evaluation: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68 else negative. HPV-positive individuals undergo further evaluation, including pelvic exams, cervical imaging, and biopsies. AVE algorithms analyze images, assigning risk scores for precancer, validated against histologic high-grade precancer. Phase 1, however, does not integrate AVE results into patient management, contrasting them with local standard care.Phase 2 effectiveness focuses on deploying AVE software and HPV genotype data in real-time clinical decision-making, evaluating feasibility, acceptability, cost-effectiveness, and health communication of the PAVE strategy in practice. Results: Currently, sites have commenced fieldwork, and conclusive results are pending. Conclusions: The study aspires to validate a screen-triage-treat protocol utilizing innovative biomarkers to deliver an accurate, feasible, and cost-effective strategy for cervical cancer prevention in resource-limited areas. Should the study validate PAVE, its broader implementation could be recommended, potentially expanding cervical cancer prevention worldwide. Funding: The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/ NIH under Grant T32CA09168.

Indexed as

Papillomavirus InfectionsUterine Cervical NeoplasmsAlgorithmsEarly Detection of CancerFemaleHumansVaginaartifitial inteligencecervical cancercost-effectivenessdigital imageepidemiologyextended genotypingglobal healthhuman papillomavirusisothernal amplificationLMIConcogenic HPVpreventionscreeningviruses

Identifiers

PMID38224340
PMCPMC10945624
OpenAlexW4387664586

What OpenQuestion holds

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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.