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Updated: Jan 21, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Using Inside Knowledge Campaign Materials to Improve Gynecologic Cancer Knowledge in Underserved Women
Mary Puckett1, Julie Townsend1, Jenny Rees Patterson2
1Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia.
Abstract:
Purpose: About 30,000 U.S. women die each year from gynecologic cancer, which disproportionately affects underserved and minority populations. This project aimed at increasing and assessing awareness of risk, symptoms, and recommended screenings and prevention activities in underserved women, through unique collaboration between the Centers for Disease Control and Prevention's (CDC) Inside Knowledge (IK) campaign, which was designed to educate women about gynecologic cancer, and the CDC's national network of organizations to reduce cancer-related disparities. Materials and Methods: CDC's national network and the IK campaign partnered to deliver tailored educational sessions about gynecologic cancer to three populations of women served by the participant organizations. Participant organizations included the National Behavioral Health Network (NBHN), Nuestras Voces (NV), and SelfMade. Pre- and post-session questionnaires were administered to assess knowledge changes. Results: Knowledge changes for risk factors, screening, and HPV vaccination varied by network organization, but all sessions increased correct identification of some symptoms. Baseline knowledge also varied among organization participants. Conclusions: Sessions were effective in increasing awareness of gynecologic cancer among underserved women; however, organizational information uptake differed. Additional resources containing specific interventions appropriate to particular underserved populations may be beneficial in increasing healthy behaviors, leading to a reduction in gynecologic cancer disparities.
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