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培训后7个月技能保持情况的客观评估:中心静脉导管置入的运动分析

Objective Assessment of Skill Retention 7 Months Post-Training: Motion Analysis of Central Venous Catheter Placement.

作者信息

Baribeau Vincent, Sane Miheer P, Sharkey Aidan, Murugappan Kadhiresan R, Walsh Daniel P, Wong Vanessa T, Mitchell John D

机构信息

is a Medical Student at Dartmouth Geisel School of Medicine, Hanover, NH. Miheer P. Sane is a Clinical Fellow in Anesthesiology at Harvard Medical School, Boston, MA, and a Pediatric Anesthesia Fellow at Boston Children's Hospital, Boston, MA. Aidan Sharkey is an Assistant Professor of Anaesthesia at Harvard Medical School, Boston, MA, and the Associate Director (Clinical) of the Center for Education Research, Technology, and Innovation (CERTAIN) at Beth Israel Deaconess Medical Center, Boston, MA. Daniel P. Walsh is an Assistant Professor of Anaesthesia at Harvard Medical School, Boston, MA; an Associate Anesthesia Residency Program Director at Beth Israel Deaconess Medical Center, Boston, MA; and the Director of the Intensive Care Unit at Beth Israel Deaconess Hospital, Plymouth, MA. Kadhiresan R. Murugappan is an Assistant Professor at RUSH University, Chicago, IL, and an Associate Anesthesiology Residency Program Director at RUSH University Medical Center, Chicago, IL. Vanessa T. Wong is an Anesthesia Critical Care Project Manager at Beth Israel Deaconess Medical Center, Boston, MA. John D. Mitchell is a Professor in the College of Human Medicine at Michigan State University, East Lansing, MI, and the Vice Chair for Academic Affairs at Henry Ford Health, Detroit, MI.

出版信息

J Educ Perioper Med. 2025 Apr 8;27(1):E742. doi: 10.46374/VolXXVII_Issue1_Mitchell. eCollection 2025 Jan-Mar.

Abstract

BACKGROUND

Central venous catheter (CVC) placement is a technically challenging skill. Routine assessment tools, including checklists and global rating scales, require subjective expert evaluation. We hypothesized that motion analysis could be used to objectively assess skill retention in CVC placement by comparing the performance of anesthesiology residents immediately after training and 7 months later.

METHODS

After learning to perform CVC placement on a mannikin, 12 first-year anesthesiology residents each performed a "baseline" trial with electromagnetic motion sensors on the dorsum of their dominant hand and base of their ultrasound probe. Seven months later, they each performed a "follow-up" mannikin trial with an identical setup. For each trial, sensors recorded participants' path length, translational motions, and rotational sum. Time was recorded for each trial as well. We defined skill retention as performance within 1 standard deviation or less of the entire cohort's average at baseline (threshold). We compared the number of residents who met the threshold, which indicated less excessive motion and therefore better performance, at baseline with the number at follow-up using McNemar's test across each metric for each sensor.

RESULTS

For path length, translational motions, and rotational sum of the probe, significantly more residents met the threshold at baseline than at follow-up ( < .04). No significant differences were detected for any metrics of the dorsum or time.

CONCLUSIONS

Motion analysis can objectively assess skill decay in anesthesiology residents performing CVC placement. Residents exhibited skill retention in tasks involving their dominant hand and skill decay in tasks involving the ultrasound probe (nondominant hand).

摘要

背景

中心静脉导管(CVC)置入是一项技术要求较高的操作技能。包括检查表和整体评分量表在内的常规评估工具需要专家进行主观评估。我们推测,通过比较麻醉科住院医师在培训后立即和7个月后的表现,运动分析可用于客观评估CVC置入技能的保持情况。

方法

在学会在人体模型上进行CVC置入后,12名一年级麻醉科住院医师每人在其优势手背部和超声探头底部使用电磁运动传感器进行一次“基线”试验。7个月后,他们每人使用相同的设置进行一次人体模型“随访”试验。对于每次试验,传感器记录参与者的路径长度、平移运动和旋转总和。每次试验也记录时间。我们将技能保持定义为在基线时(阈值)表现处于整个队列平均值的1个标准差或更低范围内。我们使用麦克尼马尔检验,比较每个传感器各指标在基线时达到阈值(表明运动过度较少,因此表现更好)的住院医师人数与随访时的人数。

结果

对于探头的路径长度、平移运动和旋转总和,在基线时达到阈值的住院医师明显多于随访时(P < .04)。在手背部或时间的任何指标上均未检测到显著差异。

结论

运动分析可客观评估麻醉科住院医师进行CVC置入时技能的衰退情况。住院医师在涉及优势手的任务中表现出技能保持,而在涉及超声探头(非优势手)的任务中表现出技能衰退。

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Motion-Tracking Machines and Sensors: Advancing Education Technology.运动追踪机器与传感器:推动教育技术发展。
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