Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy
Notice bibliographique
Résumé
Abstract Objective 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. Study design 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. Conclusion 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. PAVE Study Group Brazil Ana Ribeiro - ana-ribeiro.dantas@fiocruz.br Tainá Raiol - taina.raiol@fiocruz.br Center 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, Brazil Cambodia Te Vantha, MD, Director of Takeo Provincial Hospital,Cambodia Thay Sovannara, MD, Medical Practitioner, Raffles Medical Group, Cambodia Judith Norman, MD, Director of Women’s Health, Mercy Medical Center, Cambodia judynorman@gmail.com Dr. Andrew T. Goldstein, Director, Gynecologic Cancers Research Foundation. drg.cvvd@gmail.com Dominican Republic Margaret M. Madeleine, MPH, PhD Program in Epidemiology, Fred Hutchinson Cancer Center mmadelei@fredhutch.org Yeycy Donastorg, MD Instituto Dermatológico y Cirugía de la Piel “Dr. Huberto Bogaert Díaz”, HIV Vaccine Trials Research Unit, Santo Domingo, Dominican Republic. ydonastorg@gmail.com El Salvador Miriam 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.com Karla Alfaro, MD Basic Health International, El Salvador, kalfaro@basichealth.org Honduras Miriam 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.com Karla Alfaro, MD Basic Health International, El Salvador, kalfaro@basichealth.org . Jaqueline Figueroa, MD, Programa Nacional contra el Cáncer, Tegucigalpa, Honduras. jacqueline_figueroan@yahoo.com Eswatini Eyrun 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.no Teresa Norris, Founder and President, HPV Global Action, tnorris@hpvglobalaction.org Zeev Rosberger, PhD, Department of Oncology, Psychology and Psychiatry, McGill University, Montreal, Canada, zeev.rosberger@mcgill.ca Amelie McFadyen, MA, Chief Executive Officer, HPV Global Action, ameliemcfadyen@hpvglobalaction.org Marc Steben, MD, Ecole de Sante Publique, Université de Montréal; International society for STD research, marc@marcsteben.com Malawi Amna Haider, MD, Epidemiologist, Department of Epidemiology and Training, Epicentre, Dubai, UAE, amna.haider@epicentre.msf.org George Kassim Chilinda, MD, Médecins Sans Frontières, Operational Centre Paris, Blantyre, Malawi, gchilinda@gmail.com Henry B.K.Phiri, MD-Sexual and reproductive health department, Ministry of Health, Malawi, henryphiri06@gmail.com Nigeria Ajenifuja Kayode Olusegun, MD, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Osun state Nigeria, ajenifujako@yahoo.com Adepiti Clement Akinfolarin, MD, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Osun state Nigeria, akinfolarindepiti@yahoo.co.uk Adekunbiola Banjo, MD, College of Medicine University of Lagos, Lagos, aafbanjo@cmul.edu.ng Moharson-Bello Imran, MD, College of Medicine, University of Ibadan, Oyo state, Nigeria, imranmorhasonbello@gmail.com Oyinloye Temitope,MD, Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, Osun state, Nigeria, projectcoordinator.itoju@gmail.com Bola-Oyebamiji Sekinat, MD, College of Medicine, Osun state University, Osogbo, Osun state. Adeyemo Marydiya, MD, College of Medicine, Osun state University, Osogbo, Osun state Tanzania Karen Yeates-MD, MPH, Department of Medicine, Queen’s University, Kingston, Ontario, Canada, yeatesk@queensu.ca Safina Yuma, MD, Cervical Cancer Focal Person, Ministry of Health, Tanzania, sychande@yahoo.com Bariki Mchome, MD, Head, Reproductive Health Centre, Kilimanjaro Christian Medical Centre, Kilimanjaro, Tanzania, barikimchome@gmail.com
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,039 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».