ArticleBladder cancer (Amsterdam, Netherlands)2024
BCG therapy for bladder cancer: Exploring patient experiences and concerns through artificial intelligence-based social media analysis.
Article in Bladder cancer (Amsterdam, Netherlands), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Bilibili/TikTok videos as sources of HPV-related medical information: a cross-sectional content analysis.BMC public health · 2026Article
- Analyzing Smoking Challenges in Bladder Cancer Patients From an Online Forum: A Contemporary Qualitative Study of Peer Support and Cessation Motivators.Tobacco use insights · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is a notable disparity between the guidelines for BCG therapy in non-muscle invasive bladder cancer (NMIBC). Reddit has emerged as a popular online platform for individuals seeking information and exchanging their experiences related to bladder cancer. Objective: To investigate and classify public opinions about intravesical BCG therapy as shared on Reddit, a popular social media platform. Methods: This study employed an artificial intelligence-based approach to examine discussions related to intravesical BCG therapy on a social media platform over the past ten years. An artificial intelligence framework was developed to categorize these conversations into distinct topics and thematic categories. This framework included a partially supervised model for processing natural language (using BERT [Bidirectional Encoder Representations from Transformers]), a method for reducing data complexity, and an algorithm for clustering. Additionally, each conversation was assessed for sentiment. Results: A total of 1223 unique discussions related to BCG therapy were analyzed, comprising 110 unique posts and 1113 comments from 268 distinct authors. We identified four overarching thematic groups: 1) BCG administration procedures, (2) hesitancy in initiating or maintaining BCG treatment, (3) issues related to BCG shortage and alternative treatments, and (4) side effects of BCG treatment. Sentiment analysis of the 1223 discussions revealed that 25.2% (308) exhibited a negative sentiment, 58.3% (713) were neutral, and 16.5% (202) showed a positive sentiment. Conclusion: Online social media often contains detailed personal experiences with BCG therapy, not commonly found in medical literature. Understanding these experiences can help medical professionals improve care and treatment adherence in NMIBC.
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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.