ENT Manifestations of Wegener`s Granulomatosis

Aslam, Muhammad Javed; Aslam, Muhammad Azeem; Ahmed, Malik Iftikhar; Batool, Amna

doi:10.21649/akemu.v12i1.832

Cited by 3 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…YouTube is a video sharing platform with over two billion logged-in users (Alphabet Inc., Mountain View, CA) each month and everyday, people watch over a billion hours of videos and generate billions of views (5). YouTube allows users to upload, curate, and comment on their own and others' content.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Patient Information for Cholesteatoma on the Video Sharing Platform YouTube

et al. 2023

View full text Add to dashboard Cite

IntroductionCholesteatoma is common chronic ear condition that usually requires surgical treatment and long-term follow-up. The Internet helps patients find information about their medical conditions and assists in shared clinical decision making. This work aims to assess the quality of information on YouTube for cholesteatoma. We aim to assess the quality of the most popular cholesteatoma videos on YouTube using recognized scoring systems and determine if quality correlated with video likes and views.MethodsThe YouTube website was systematically searched on separate days with a formal search strategy to identify videos relevant to cholesteatoma. Each video was viewed and scored by three independent assessors using a novel Essential and Ideal Video Completeness criteria for cholesteatoma and the validated DISCERN tool. Popularity metrics were analyzed and compared with video popularity.ResultsA total of 90 YouTube videos were analyzed with an average 55,292 views per video with an average of 271 likes and 22 dislikes. The interrater correlation was moderate with Fleiss kappa score 0.42 (p < 0.01) using the Essential and Ideal Video Completeness criteria for cholesteatoma and interrater correlation coefficient was 0.78 (95% confidence interval, 0.58–0.90), indicating good reliability for DISCERN scores. The overall video quality was poor with higher DISCERN scores found from academic institutions.ConclusionYouTube content with regard to cholesteatoma is of poor quality. Videos with unclassified sources or more dislikes correlated with poor quality. Otology societies should be encouraged to publish high-quality YouTube videos on cholesteatoma and other ear conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Patient Information for Cholesteatoma on the Video Sharing Platform YouTube

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Twitter was established in 2006 and is one of the largest interactive social media platforms in the world with an estimated 217 million worldwide monetizable daily active users at the end of 2021 (2). This is nearly double its active users in 2017 of 110 million.…”

Section: Introductionmentioning

confidence: 99%

“…This is nearly double its active users in 2017 of 110 million. It is estimated that a little over one in five adults in the United States use Twitter, with most of Twitter’s audience falling in the 25- to 34-year-old age-group (38.5%) (2).…”

Section: Introductionmentioning

confidence: 99%

Public Perception of Ménière’s Disease: A Social Media Analysis

et al. 2022

View full text Add to dashboard Cite

ObjectiveTo analyze tweets associated with Ménière’s disease (MD), including type of users who engage, change in usage patterns, and temporal associations, and to compare the perceptions of the general public with healthcare providers.MethodsAn R-program code, academictwitterR API, was used to query Twitter. All tweets mentioning MD from 2007 to 2021 were retrieved and analyzed. Valence Aware Dictionary and Sentiment Reasoning was used as a model to assess sentiment of tweets. Two reviewers assessed 1,007 tweets for qualitative analysis, identifying the source and the topic of the tweet.ResultsA total of 37,402 tweets were analyzed. The number of tweets per user ranged from 1 to 563 (M = 33.7, SD = 91.1). Quantitative analysis showed no temporal or seasonal association; however, tweeting increased when celebrities were diagnosed with MD. Of the 1007 representative tweets analyzed, 60.6% of tweets came from the general public and were largely of negative sentiment focusing on quality of life and support, whereas healthcare providers accounted for 23% of all tweets and focused on treatment/prevention. Tweets by news sources accounted for the remaining 13% of all tweets and were primarily positive in sentiment and focused on awareness.ConclusionsMD is commonly tweeted about by the general public, with limited input regarding the disease from healthcare providers. Healthcare providers must provide accurate information and awareness regarding MD, especially when awareness is highest, such as when celebrities are diagnosed.Level of EvidenceLevel IV.Indicate IRB or IACUCNot applicable.

show abstract

“…YouTube is the first web-based video sharing platform. With more than 2 billion users and 1 billion hours of videos watched every day, YouTube has become one of the largest video sharing platforms worldwide, and it has played an essential role in public communication during the COVID-19 epidemic [ 8 ]. Comments on YouTube have provided rich data for public discourse and sentiment research.…”

Section: Introductionmentioning

confidence: 99%

Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

Zheng¹,

Xue²,

Sun³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

show abstract

ENT Manifestations of Wegener`s Granulomatosis

Cited by 3 publications

References 2 publications

Assessing the Quality of Patient Information for Cholesteatoma on the Video Sharing Platform YouTube

Assessing the Quality of Patient Information for Cholesteatoma on the Video Sharing Platform YouTube

Public Perception of Ménière’s Disease: A Social Media Analysis

Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

Contact Info

Product

Resources

About