Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Aim: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. Eye movement may be a promising marker of arousal recovery, as pathways for eye movement and arousal share common anatomic structures.

Parental Views on the Role of Social Media in Pediatric Otolaryngology.

Objective: Social media is playing an increasingly important role in medicine as a tool for patients and their families to find information and connect with others. The goal of this study is to understand parental views on if and how social media should be incorporated into pediatric otolaryngology by physicians and hospitals.

Methods: A survey was distributed to parents of pediatric otolaryngologic patients to assess views on professional social media use by physicians and hospitals.