Evaluation of the Educational Quality of the Top 30 Most Viewed Laparoscopic Distal Pancreatectomy Videos on YouTube.

Background: Online surgical videos serve as useful adjuncts for surgical training. YouTube is a social media platform increasingly used for education. However, the educational qualities of these videos have not been proven. This study aims to review the topmost 30 viewed videos on laparoscopic distal pancreatectomy (LDP) on YouTube. Methods: A YouTube search was performed on August 1, 2020, using the term "laparoscopic distal pancreatectomy." Inclusion criteria were LDP with or without splenectomy. Exclusion criteria were open or robotic distal pancreatectomy and radical antegrade modular pancreatosplenectomy. To grade the videos, we used a modified version of the LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) 24 of the original 37 consensus statements were used in our study. Results: Twenty-nine of the top 30 most viewed videos were included (1 video was a duplicate). The overall mean view count and number of likes were 7195 (range 2322-39,927) and 17 (range 0-108), respectively. Thirteen videos (44.8%) were on LDP with splenic preservation. The mean conformity to the modified LAP-VEGaS was 9.3 (range 4-16) with a mean of 38.8% (range 16.7%-66.7%) of the criteria met. There was weak correlation between the number of views and the number of criteria met (ρ = 0.189, P < .05). Twelve videos (41.4%) described about staple line management, nine videos (31%) about tips and tricks to reduce postoperative pancreatic fistula, including drainage tube management, and three videos (10.3%) identified the common hepatic artery. Videos with no commentary had the lowest mean percentage of criteria met (24.6%). Conclusion: The topmost viewed LDP surgical videos have gaps in meeting the educational needs of a trainee. Video uploaders should be cognizant of the learning needs of surgical trainees.

Maximum expected entropy transformed Latin hypercube designs.

Existing projection designs (e.g. maximum projection designs) attempt to achieve good space-filling properties in all projections. However, when using a Gaussian process (GP), model-based design criteria such as the entropy criterion is more appropriate. We employ the entropy criterion averaged over a set of projections, called expected entropy criterion (EEC), to generate projection designs. We show that maximum EEC designs are invariant to monotonic transformations of the response, i.e. they are optimal for a wide class of stochastic process models. We also demonstrate that transformation of each column of a Latin hypercube design (LHD) based on a monotonic function can substantially improve the EEC. Two types of input transformations are considered: a quantile function of a symmetric Beta distribution chosen to optimize the EEC, and a nonparametric transformation corresponding to the quantile function of a symmetric density chosen to optimize the EEC. Numerical studies show that the proposed transformations of the LHD are efficient and effective for building robust maximum EEC designs. These designs give projections with markedly higher entropies and lower maximum prediction variances (MPV's) at the cost of small increases in average prediction variances (APV's) compared to state-of-the-art space-filling designs over wide ranges of covariance parameter values.