ArticleSurgical neurology international2026
Manufacturer and User Facility Device Experience (MAUDE)-Dash: An open-source, interactive dashboard for real-time post-market surveillance of medical devices.
Article in Surgical neurology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
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1 author.
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Abstract
Background: The Manufacturer and User Facility Device Experience (MAUDE) database is a cornerstone of post-market medical device surveillance in the United States, yet its large size, heterogeneous formats, and complex relational structure make it difficult for clinicians and researchers without advanced programming skills to use. This study describes the design and implementation of MAUDE-Dash, an open-source, interactive dashboard that streamlines the ingestion, management, and analysis of MAUDE data. Methods: MAUDE-Dash utilizes a two-layer Python-based architecture. A dedicated ingestion pipeline normalizes and loads the public MAUDE text files into a single, efficient analytical database built on DuckDB. A Streamlit web application provides accessible modules for filtering reports, computing key performance indicators, visualizing temporal trends, characterizing patient demographics, interpreting device and patient problem codes, and performing narrative text analysis. Results: In a demonstration use case involving pedicle screw systems, MAUDE-Dash rapidly identified thousands of relevant reports from 2018 to 2023. The dashboard enabled interactive exploration of yearly report volumes, distributions of event types, predominant manufacturers, common problem codes, and recurrent narrative terms (e.g., screw fracture, loosening). The complete analytic workflow, from query definition to denormalized dataset export, was achieved within minutes, significantly reducing technical barriers and analytic time compared to traditional methods. Conclusion: MAUDE-Dash operationalizes an efficient, reproducible framework for post-market surveillance. By integrating an optimized local database with an accessible, modular interface, it democratizes large-scale signal exploration and hypothesis generation, providing an extensible platform for advanced safety analyses.
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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.