Objective. To compare pharmacy students' performance in a therapeutics course after attending live lectures and/or viewing video-recorded lectures. Methods. Attendance was taken during seven lectures spaced equally throughout the therapeutics course. Data on students' viewing of the video-recorded lectures was extracted. Students were grouped based on class attendance and video-viewing behavior; these data were correlated to student performance on examination lecture specific material. The data were also evaluated based on students' final course grade. Results. From each lecture for which data were collected, between 346 and 349 students were included in the analysis, resulting in 2,430 data points. Students who were attended lecture and did not access the video-recorded lecture were associated with better performance on the respective examination than students who were absent and accessed the video-recorded lecture only once (grade571.0 vs 62.3). Students who attended lecture, regardless of whether they subsequently viewed the video online, were associated with better performance on the examination than students who were absent (70.4 vs 64.0). Among all students who attended lecture, those that also used the video-recorded lecture were associated with similar performance on the examination as those who did not access the video (grade569.1 vs 71.0). Conclusion.Results from this pilot study demonstrated that live class attendance was associated with higher examination performance than viewing recorded lectures for a therapeutics class. The results of this pilot study can be used to guide future research in understanding how teaching methods affect student performance.
Frequently, patients on anticoagulation therapy who require surgery or other invasive procedures are not considered for bridging therapy. These patients present clinicians with a number of complicated issues to consider, as clinicians must weigh the risk of thrombosis and its potentially devastating consequences to the risk of bleeding from bridging medication. Currently, many clinicians are not well informed about the options for and efficacy of bridging therapy.Traditional bridge therapy for patients receiving longterm oral anticoagulation often involves weight-adjusted intravenous unfractionated heparin (UFH) during the temporary discontinuation of the oral anticoagulant. We sought to develop an outpatient-based disease management protocol with low-molecular-weight heparin (LMWH) as bridge therapy to prevent unnecessary hospitalizations and reduce cost to the patient.We used a multi-step protocol to assess 126 patients over a 7-month period. Initially, patient INRs were determined based on specific information regarding the reason for anticoagulation therapy. A risk score was generated. In addition, the procedure was graded based on bleeding risk and detailed start and stop times for warfarin and LMWH were determined. These issues along with exclusion criteria for bridging were used to decide if bridging therapy was appropriate. Of the 126 patients assessed, 55 (43.7%) patients were recommended for bridging therapy. Patients were placed on an enoxaprin or heparin protocol based on the number of days until the procedure, the reason for anticoagulation therapy, and social issues such as noncompliance, language barriers, or inability to administer medication. Patient outcomes were monitored post procedure at 7 and 30 days. Outcomes were measured by thromboembolism, major bleed event, death, and minor bleed event. The protocol has proved successful with only three adverse events (2%) reported: one adverse event was unrelated to the bridging protocol and the other two were minor bleeds.
Gaze and face tracking algorithms have traditionally battled a compromise between computational complexity and accuracy; the most accurate neural net algorithms cannot be implemented in real time, but less complex realtime algorithms suffer from higher error. This project seeks to better bridge that gap by improving on real-time eye and facial recognition algorithms in order to develop accurate, real-time gaze estimation with an emphasis on minimizing training data and computational complexity. Our goal is to use eye and facial recognition techniques to enable users to perform limited tasks based on gaze and facial input using only a standard, low-quality web cam found in most modern laptops and smart phones and the limited computational power and training data typical of those scenarios. We therefore identified seven promising, fundamentally different algorithms based on different user features and developed one outstanding, one workable, and one honorable mention gaze tracking pipelines that match the performance of modern gaze trackers while using no training data.
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