A prospective randomized study assessing optimal method for teaching vascular anastomoses.

BACKGROUND: Laboratory skills training is now required for general surgery residents. The optimal method of teaching vascular anastomosis (VA) is not well defined. Teaching VA skills one-on-one with a faculty instructor will result in a more rapid accumulation of skills than teaching in a large group setting. METHODS: Residents were shown an instructional video on how to perform a VA using a standardized model (cadaver saphenous vein and porcine aorta). Each resident then performed a baseline VA. Sixteen first- and second-year surgical residents were then randomized to 2 VA teaching sessions that consisted of either 1) group teaching (GT, 8 residents in a room with 1 faculty instructor circulating) or 2) one-on-one teaching (1-on-1, faculty member focused on individual resident). After each of these sessions, residents performed a standardized VA. The anastomoses were video recorded. Performance was evaluated using a standardized scoring system by a separate expert who viewed the video recordings in a blinded fashion. Outcome measures included total errors, total time, global rating scale, and an anastomosis-specific end-product evaluation (leak and passage of coronary dilator). RESULTS: Overall, significant decreases in total errors (21 to 15, P=0.001) and time to complete anastomoses (42 to 38 min, P=0.02) and an increase in global rating scales (7 to 11, P=0.003) were noted in both groups from baseline after 2 VA teaching session. The 1-on-1 group demonstrated significantly greater improvement in terms of reduced anastomotic time (30 vs. 42 min, P=0.007) and in reduction of errors (13 vs. 19 errors, P=0.09) than the GT group. CONCLUSIONS: The high-fidelity VA model is a useful tool for junior general surgery residents. Both GT and 1-on-1 groups demonstrated significant improvement in total errors and time after only 2 sessions. Greater improvement was noted using the 1-on-1 model.

Clipper: p-value-free FDR control on high-throughput data from two conditions.

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.