ArticleAEM education and training2018
Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.
Article in AEM education and training, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Assessment of a brief point-of-care ultrasound training workshop among a large cohort of medical students.Quantitative imaging in medicine and surgery · 2025Article
- Learning curves for point-of-care ultrasound image acquisition for novice learners in a longitudinal curriculum.The ultrasound journal · 2023Article
- Assessing the learning curve of robot-assisted total mesorectal excision: a multicenter study considering procedural safety, pathological safety, and efficiency.International journal of colorectal disease · 2023Article
- Article
- The ultrasound competency assessment tool for four-view cardiac POCUS.The ultrasound journal · 2021Article
- Longitudinal accuracy analysis of ultrasound performed during a four-year emergency medicine residency.AEM education and training · 2021Article
- Tools for Measuring Clinical Ultrasound Competency: Recommendations From the Ultrasound Competency Work Group.AEM education and training · 2020Article
- Learning Analytics in Medical Education Assessment: The Past, the Present, and the Future.AEM education and training · 2018Article
- Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.AEM education and training · 2018Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUltrasound (US) has been a regular practice in emergency departments for several decades. Thus, train our students to US is of prime interest. Because US image acquisition ability can be very different from a patient to another (depending on image quality), it seems relevant to adapt US learning curves (LCs) to patient image quality using tools based on cumulative summation (CUSUM) as the risk-adjusted LC CUSUM (RLC).
objectivesThe aim of this study was to monitor LC of medical students for the acquisition of abdominal emergency US views and to adapt these curves to patient image quality using RLC.
methodsWe asked medical students to perform abdominal US examinations with the acquisition of 11 views of interest on emergency patients after a learning session. Emergency physicians reviewed the student examinations for validation. LCs were plotted and the student was said proficient for a specific view if his LC reached a predetermined limit fixed by simulation.
resultsSeven students with no previous experience in US were enrolled. They performed 19 to 50 examinations of 11 views each. They achieve proficiency for a median of 9 (6-10) views. Aorta and right pleura views were validated by seven students; inferior vena cava, right kidney, and bladder by six; gallbladder and left kidney by five; portal veins and portal hilum by four; and subxyphoid and left pleura by three. The number of US examinations required to reach proficiency ranged from five to 41 depending on the student and on the type of view. LC showed that students reached proficiency with different learning speeds.
conclusionsThis study suggests that, when monitoring LCs for abdominal emergency US, there is some heterogeneity in the learning process depending on the student skills and the type of view. Therefore, rules based on a predetermined number of examinations to reach proficiency are not satisfactory.
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Registered trials
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