随机对照试验
价值(数学)
医学
超声波
医学物理学
物理疗法
放射科
统计
内科学
数学
作者
Lisbeth Anita Andreasen,Ann Tabor,Lone Nikoline Nørgaard,Charlotte Ringsted,Puk Sandager,Susanne Rosthøj,Martin G. Tolsgaard
摘要
ABSTRACT Objective To explore the effects of simulation‐based ultrasound training on the accuracy of fetal weight estimation in the third trimester among obstetricians with different levels of clinical experience. Methods This was a multicenter, randomized pre–post‐test practical trial conducted between March 2016 and January 2018. Obstetricians with different levels of clinical experience were randomized to either simulation‐based ultrasound training focusing on fetal weight scans or no intervention. Participants completed two scans in pregnant women at term to establish baseline accuracy of fetal weight estimation. Another two scans were performed at follow‐up. Accuracy was defined by the percentage difference between estimated fetal weight and actual birth weight. Ultrasound image quality was rated by two expert raters. Results Seventy participants with different levels of clinical experience completed the study. Adjusting for clinical experience, the intervention group demonstrated an improvement in measurement accuracy of 31.9% (95% CI, 6.9–50.1%) ( P = 0.02), whereas the control group did not improve (relative difference, 13.1% (95% CI, −17.9 to 55.9%); P = 0.45). The change in accuracy was significantly different between the groups ( P = 0.02) and independent of clinical experience ( P = 0.54). Image‐quality scores improved by a mean of 1.2 (95% CI, 0.4–2.1) ( P < 0.01) in the intervention group, with no change in the control group (mean difference, 0.1 (95% CI, −0.8 to 1.0); P = 0.78). There was a strong negative correlation between time spent using the simulator and clinical experience ( r = −0.70, P = 0.0001). Conclusion Simulation‐based ultrasound training improved accuracy and image quality when performing fetal weight estimation in women at term, independent of obstetricians' clinical experience. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.
科研通智能强力驱动
Strongly Powered by AbleSci AI