Publications by authors named "Jesse M Rideout"
Article Synopsis
- Early detection of COVID-19 is crucial for controlling transmission, and consumer wearables like the Oura Ring can help by tracking physiological metrics and gathering user-reported data.
- In a study with over 63,000 participants, a machine learning algorithm successfully predicted COVID-19 onset an average of 2.75 days before testing, achieving a sensitivity of 82% and specificity of 63%.
- The algorithm's accuracy improved when including continuous temperature data, and results showed variations based on age and sex, emphasizing the need for diverse representation in detection technology development.
Aims: A multicenter simulation-based research study to assess the ability of interprofessional code-teams and individual members to perform high-quality CPR (HQ-CPR) at baseline and following an educational intervention with a CPR feedback device.
Methods: Five centers recruited ten interprofessional teams of AHA-certified adult code-team members with a goal of 200 participants. Baseline testing of chest compression (CC) quality was measured for all individuals.
Background: Mastering laparoscopic surgical skills requires considerable time and effort. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PT(©)) is being developed as a computerized version of the peg transfer task of the Fundamentals of Laparoscopic Surgery (FLS) system using virtual reality technology. We assessed the learning curve of trainees on the VBLaST-PT(©) using the cumulative summation (CUSUM) method and compared them with those on the FLS to establish convergent validity for the VBLaST-PT(©).
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