RESUMEN
BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.
RESUMEN
Primary nonfunction (PNF) accounts for 0.6 to 8% of renal allograft failure, and the focus on causes of PNF has changed from rejection to other causes. Calcium oxalate (CaOx) deposition is common in early allograft biopsies, and it contributes in moderate intensity to higher incidence of acute tubular necrosis and poor graft survival. A-49-year old male with ESRD secondary to polycystic kidney disease underwent extended criteria donor kidney transplantation. Posttransplant, patient developed delayed graft function (DGF), and the biopsy showed moderately intense CaOx deposition that persisted on subsequent biopsies for 16 weeks, eventually resulting in PNF. The serum oxalate level was 3 times more than normal at 85 µmol/L (normal <27 µmol/L). Allograft nephrectomy showed massive aggregates of CaOx crystal deposition in renal collecting system. In conclusion, acute oxalate nephropathy should be considered in the differential diagnosis of DGF since optimal management could change the outcome of the allograft.