Reviewer #3 (Public Review): Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy
Bibliographic record
Abstract
To describe the HPV-Automated Visual Evaluation (PAVE) Study, an international, multi-centric study designed to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (Large Loop Excision of the Transformation Zone). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine implementation of the PAVE strategy into clinical practice, cost-effectiveness, and health communication.Phase 1 Efficacy: Nonpregnant women, aged 25-49 years, without prior hysterectomy, are being screened at nine study sites in resource-limited settings. Eligible and consenting participants perform self-collection of vaginal specimens for HPV testing using a FLOQSwab (Copan). Swabs are transported dry and undergo testing for HPV using a newly-redesigned isothermal DNA amplification HPV test (ScreenFire), which has been designed to provide HPV genotyping by hierarchical risk groups: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68. HPV-negative individuals are considered negative for precancer/cancer and do not undergo further testing. HPV-positive individuals undergo pelvic examination with collection of cervical images and targeted biopsies of all acetowhite areas or endocervical sampling in the absence of visible lesions. Cervical images are used to refine a deep learning AVE algorithm that classifies images as normal, indeterminate, or precancer+. AVE classifications are validated against the histologic endpoint of high-grade precancer determined by biopsy. The combination of HPV genotype and AVE classification is used to generate a risk score that corresponds to the risk of precancer (lower, medium, high, highest). During the efficacy phase, clinicians and patients will receive HPV testing results but not AVE results or risk scores. Treatment during the efficacy phase will be performed per local standard of care: positive Visual Inspection with Acetic Acid impression, high-grade colposcopic impression or CIN2+ on colposcopic biopsy, HPV positivity, or HPV 16,18/45 positivity. The sensitivity of the PAVE strategy for detection of precancer will be compared to current SOC at a given level of specificity.Phase 2 Effectiveness: The AVE software will be downloaded to the new dedicated image analysis and thermal ablation devices (Liger Iris) into which the HPV genotype information can be entered to provide risk HPV-AVE risk scores for precancer to clinicians in real time. The effectiveness phase will examine clinician use of the PAVE strategy in practice, including feasibility and acceptability for clinicians and patients, cost-effectiveness, and health communication.The goal of the PAVE study is to validate a screen-triage-treat protocol using novel biomarkers to provide an accurate, feasible, cost-effective strategy for cervical cancer prevention in resource-limited settings.Ana Ribeiro - ana-ribeiro.dantas@fiocruz.brTainá Raiol - taina.raiol@fiocruz.brCenter for Women’s Integrated Health, Oswaldo Cruz Foundation (Fiocruz), Brasília, DF, Brazil.MARCO Clinical and Molecular Research Center, University Hospital of Brasília/EBSERH, Federal District, BrazilTe Vantha, MD, Director of Takeo Provincial Hospital,CambodiaThay Sovannara, MD, Medical Practitioner, Raffles Medical Group, CambodiaJudith Norman, MD, Director of Women’s Health, Mercy Medical Center, Cambodia judynorman@gmail.comDr. Andrew T. Goldstein, Director, Gynecologic Cancers Research Foundation. drg.cvvd@gmail.comMargaret M. Madeleine, MPH, PhDProgram in Epidemiology, Fred Hutchinson Cancer Centermmadelei@fredhutch.orgYeycy Donastorg, MDInstituto Dermatológico y Cirugía de la Piel “Dr. Huberto Bogaert Díaz”, HIV Vaccine Trials Research Unit, Santo Domingo, Dominican Republic. ydonastorg@gmail.comMiriam Cremer MD; Basic Health International, Pittsburgh, PA 15205, USA. Ob/Gyn and Women’s Health Institute, Cleveland Clinic, Cleveland, OH 44195, USA. miriam.cremer@gmail.comKarla Alfaro, MD Basic Health International, El Salvador, kalfaro@basichealth.orgMiriam Cremer MD; Basic Health International, Pittsburgh, PA 15205, USA. Ob/Gyn and Women’s Health Institute, Cleveland Clinic, Cleveland, OH 44195, USA. miriam.cremer@gmail.comKarla Alfaro, MD Basic Health International, El Salvador, kalfaro@basichealth.org.Jaqueline Figueroa, MD, Programa Nacional contra el Cáncer, Tegucigalpa, Honduras. jacqueline_figueroan@yahoo.comEyrun F. Kjetland, MD, PhD, Professor, Departments of Global Health and Infectious Diseases Ullevaal, Centre for imported and Tropical Diseases, Oslo University Hospital Ullevaal, Oslo, Norway; College of Health Sciences, Discipline of Public Health, Nelson Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa;Centre for Bilharzia and Tropical Health Research (non-profit), BRIGHT Academy, Durban, South Africa e.f.kjetland@medisin.uio.noTeresa Norris, Founder and President, HPV Global Action, tnorris@hpvglobalaction.orgZeev Rosberger, PhD, Department of Oncology, Psychology and Psychiatry, McGill University, Montreal, Canada, zeev.rosberger@mcgill.caAmelie McFadyen, MA, Chief Executive Officer, HPV Global Action, ameliemcfadyen@hpvglobalaction.orgMarc Steben, MD, Ecole de Sante Publique, Université de Montréal; International society for STD research, marc@marcsteben.comAmna Haider, MD, Epidemiologist, Department of Epidemiology and Training, Epicentre, Dubai, UAE, amna.haider@epicentre.msf.orgGeorge Kassim Chilinda, MD, Médecins Sans Frontières, Operational Centre Paris, Blantyre, Malawi, gchilinda@gmail.comHenry B.K.Phiri, MD-Sexual and reproductive health department, Ministry of Health, Malawi, henryphiri06@gmail.comAjenifuja Kayode Olusegun, MD, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Osun state Nigeria, ajenifujako@yahoo.comAdepiti Clement Akinfolarin, MD, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Osun state Nigeria, akinfolarindepiti@yahoo.co.ukAdekunbiola Banjo, MD, College of Medicine University of Lagos, Lagos, aafbanjo@cmul.edu.ngMoharson-Bello Imran, MD, College of Medicine, University of Ibadan, Oyo state, Nigeria, imranmorhasonbello@gmail.comOyinloye Temitope,MD, Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, Osun state, Nigeria, projectcoordinator.itoju@gmail.comBola-Oyebamiji Sekinat, MD, College of Medicine, Osun state University, Osogbo, Osun state.Adeyemo Marydiya, MD, College of Medicine, Osun state University, Osogbo, Osun stateKaren Yeates-MD, MPH, Department of Medicine, Queen’s University, Kingston, Ontario, Canada, yeatesk@queensu.caSafina Yuma, MD, Cervical Cancer Focal Person, Ministry of Health, Tanzania, sychande@yahoo.comBariki Mchome, MD, Head, Reproductive Health Centre, Kilimanjaro Christian Medical Centre, Kilimanjaro, Tanzania, barikimchome@gmail.comAlex Mremi, MD, Head, Department of Pathology, Kilimanjaro Christian Medical Centre, Kilimanjaro, Tanzania, alexmremi@gmail.com
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.021 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".