■几种盆腔区域癌症的发病率很高,他们的手术治疗会导致不良反应,如尿失禁和大便失禁,显著影响患者生活质量。手术后尿失禁是一个重要的问题,尿失禁的患病率为25%至45%,大便失禁的患病率为9-68%。癌症幸存者越来越多地将YouTube作为与他人联系的平台,然而,由于错误信息普遍存在,因此需要谨慎行事。
■本研究旨在评估YouTube视频中有关骨盆区癌症手术后术后失禁的信息质量。
■YouTube搜索“癌症手术后失禁”产生了108个视频,随后进行了分析。为了评估这些视频,使用了几种质量评估工具,包括DISCERN,GQS,JAMA,PEMAT,和MQ-VET。统计分析,如描述性统计和相互关联测试,被用来评估各种视频属性,包括特征,人气,教育价值,质量,和可靠性。此外,像PCA这样的人工智能技术,t-SNE,和UMAP用于数据分析。HeatMap和分层聚类树图技术验证了机器学习结果。
■质量量表呈现了高度的相关性(p<0.01),基于人工智能的技术呈现了数据集样本的清晰聚类表示,通过热图和分层聚类树状图得到了加强。
■关于“癌症手术后失禁”的YouTube视频在多个尺度上呈现“高”质量。使用AI工具,像PCA,t-SNE,和UMAP,突出显示了对大型健康数据集的聚类,改善数据可视化,模式识别,和复杂的医疗保健分析。
UNASSIGNED: Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients\' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9-68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.
UNASSIGNED: This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.
UNASSIGNED: A YouTube search for \"Incontinence after cancer surgery\" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.
UNASSIGNED: The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.
UNASSIGNED: YouTube videos on \"Incontinence after Cancer Surgery\" present a \"High\" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.