ABSTRACT
Acute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients' outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry ( http://www.reviewregistry1727 .).
ABSTRACT
INTRODUCTION: The use of social media as a medical information tool parallels rising obesity rates. TikTok, the popular video-sharing platform, contains nearly 99,000 videos hashtagged "weightloss." Prior studies have analyzed the quality of medical information on TikTok in other areas of medicine. However, the quality of videos regarding weight loss procedures has not yet been determined. METHODS: Hashtags encompassing three weight loss modalities were searched using TikTok's algorithm. The first 50 videos meeting inclusion criteria for each modality were considered. Two independent reviewers categorized videos and assessed their content quality using DISCERN. Quality scores and popularity were compared between videos sources, modalities, and content categories. RESULTS: Of 150 videos included, 20.7% were created by physicians versus 79.3% by non-physicians (p < 0.001). The average DISCERN score for physician-created content was significantly higher than that of non-physicians (p < 0.001), despite significantly less popularity (p < 0.002). The 50 most popular videos had significantly lower DISCERN scores than the 50 least popular (p < 0.02). The average DISCERN score for endoscopic sleeve gastroplasty (ESG) videos were significantly higher than videos related to Roux-en-Y gastric bypass (RYGB) and vertical sleeve gastrectomy (VSG) (p < 0.001). VSG-related videos were significantly more popular than RYGB- and ESG-related videos (p < 0.001 and p < 0.001). Finally, educational videos had significantly higher DISCERN scores than weight loss transformation and personal experience videos (p < 0.001). CONCLUSION: Videos on TikTok related to weight loss procedures are poor, and greater popularity trends with lower quality. Assessment of content can encourage viewers to seek better information and allow providers to improve patient information tools.
Subject(s)
Gastric Bypass , Gastroplasty , Obesity, Morbid , Social Media , Humans , Obesity, Morbid/surgery , Gastric Bypass/methods , Gastroplasty/methods , Weight LossABSTRACT
OBJECTIVE: The use of social media applications to disseminate information has substantially risen in recent decades. Spine and back pain-related hashtags have garnered several billion views on TikTok. As such, these videos, which share experiences, offer entertainment, and educate users about spinal surgery, have become increasingly influential. Herein, we assess the quality of spine surgery content TikTok from providers and patients. METHODS: Fifty hashtags encompassing spine surgery ("#spinalfusion," "#scoliosissurgery," and "#spinaldecompression") were searched using TikTok's algorithm and included. Two independent reviewers rated the quality of each video via the DISCERN questionnaire. Video metadata (likes, shares, comments, views, length) were all collected; type of content creator (musculoskeletal, layperson) and content category (educational, patient experience, entertainment) were determined. RESULTS: The overall DISCERN score was, on average, 24.4. #Spinalfusion videos demonstrated greater engagement, higher average likes (p = 0.02), and more comments (p < 0.001) compared to #spinaldecompression and #scoliosissurgery. #Spinaldecompression had the highest DISCERN score (p < 0.001), likely explained by the higher percentage of videos that were educational (p < 0.001) and created by musculoskeletal (MSK) professionals (p < 0.001). Compared to laypersons, MSK professionals had significantly higher quality videos (p < 0.001). Similarly, the educational category demonstrated higher quality videos (p < 0.001). Video interaction trended lower with MSK videos and educational videos had the lowest interaction of the content categories (likes: p = 0.023, comments: p = 0.005). CONCLUSION: The quality of spine surgery videos on TikTok is low. As the influence of the new social media landscape governs how the average person consumes information, MSK providers should participate in disseminating high-quality content.
