Abstract：
OBJECTIVE: To evaluate human-based Medical Subject Headings (MeSH) allocation in articles about \'patient simulation\'-a technique that mimics real-life patient scenarios with controlled patient responses.
METHODS: A validation set of articles indexed before the Medical Text Indexer-Auto implementation (in 2019) was created with 150 combinations potentially referring to \'patient simulation\'. Articles were classified into four categories of simulation studies. Allocation of seven MeSH terms (Simulation Training, Patient Simulation, High Fidelity Simulation Training, Computer Simulation, Patient-Specific Modelling, Virtual Reality, and Virtual Reality Exposure Therapy) was investigated. Accuracy metrics (sensitivity, precision, or positive predictive value) were calculated for each category of studies.
RESULTS: A set of 7213 articles was obtained from 53 different word combinations, with 2634 excluded as irrelevant. \'Simulated patient\' and \'standardized/standardized patient\' were the most used terms. The 4579 included articles, published in 1044 different journals, were classified into: \'Machine/Automation\' (8.6%), \'Education\' (75.9%) and \'Practice audit\' (11.4%); 4.1% were \'Unclear\'. Articles were indexed with a median of 10 MeSH (IQR 8-13); however, 45.5% were not indexed with any of the seven MeSH terms. Patient Simulation was the most prevalent MeSH (24.0%). Automation articles were more associated with Computer Simulation MeSH (sensitivity = 54.5%; precision = 25.1%), while Education articles were associated with Patient Simulation MeSH (sensitivity = 40.2%; precision = 80.9%). Practice audit articles were also polarized to Patient Simulation MeSH (sensitivity = 34.6%; precision = 10.5%).
CONCLUSIONS: Inconsistent use of free-text words related to patient simulation was observed, as well as inaccuracies in human-based MeSH assignments. These limitations can compromise relevant literature retrieval to support evidence synthesis exercises.
METHODS: A validation set of articles indexed before the Medical Text Indexer-Auto implementation (in 2019) was created with 150 combinations potentially referring to \'patient simulation\'. Articles were classified into four categories of simulation studies. Allocation of seven MeSH terms (Simulation Training, Patient Simulation, High Fidelity Simulation Training, Computer Simulation, Patient-Specific Modelling, Virtual Reality, and Virtual Reality Exposure Therapy) was investigated. Accuracy metrics (sensitivity, precision, or positive predictive value) were calculated for each category of studies.
RESULTS: A set of 7213 articles was obtained from 53 different word combinations, with 2634 excluded as irrelevant. \'Simulated patient\' and \'standardized/standardized patient\' were the most used terms. The 4579 included articles, published in 1044 different journals, were classified into: \'Machine/Automation\' (8.6%), \'Education\' (75.9%) and \'Practice audit\' (11.4%); 4.1% were \'Unclear\'. Articles were indexed with a median of 10 MeSH (IQR 8-13); however, 45.5% were not indexed with any of the seven MeSH terms. Patient Simulation was the most prevalent MeSH (24.0%). Automation articles were more associated with Computer Simulation MeSH (sensitivity = 54.5%; precision = 25.1%), while Education articles were associated with Patient Simulation MeSH (sensitivity = 40.2%; precision = 80.9%). Practice audit articles were also polarized to Patient Simulation MeSH (sensitivity = 34.6%; precision = 10.5%).
CONCLUSIONS: Inconsistent use of free-text words related to patient simulation was observed, as well as inaccuracies in human-based MeSH assignments. These limitations can compromise relevant literature retrieval to support evidence synthesis exercises.
摘要：
目的：在关于“患者模拟”的文章中评估基于人类的医学主题词（MeSH）分配，这是一种模拟现实生活中患者情景并控制患者反应的技术。
方法：创建了在医疗文本索引器-自动实施（2019年）之前索引的验证文章集，其中150种组合可能涉及“患者模拟”。文章分为四类模拟研究。七个MeSH术语的分配(模拟训练，患者模拟,高保真模拟训练,计算机模拟,患者特定模型，虚拟现实,和虚拟现实暴露疗法)进行了研究。准确性指标(灵敏度，精度,或阳性预测值)计算每个类别的研究。
结果：从53种不同的单词组合中获得了一组7213篇文章，2634被排除为无关紧要。“模拟患者”和“标准化/标准化患者”是最常用的术语。4579条包括文章，发表在1044种不同的期刊上，被分类为：“机器/自动化”（8.6%），“教育”（75.9%）和“实践审计”（11.4%）；4.1%为“不清楚”。文章的索引中位数为10MeSH(IQR8-13)；然而，45.5%的人没有使用七个MeSH术语中的任何一个进行索引。患者模拟是最普遍的MeSH（24.0%）。自动化文章与计算机模拟MeSH更相关（灵敏度=54.5%；精度=25.1%），而教育文章与患者模拟MeSH相关（灵敏度=40.2%；精度=80.9%）。实践审核文章也被极化为患者模拟MeSH（灵敏度=34.6％；精度=10.5％）。
结论：观察到与患者模拟相关的自由文本单词的使用不一致，以及基于人类的MeSH分配中的不准确性。这些限制可能会损害相关文献检索以支持证据综合练习。
方法：创建了在医疗文本索引器-自动实施（2019年）之前索引的验证文章集，其中150种组合可能涉及“患者模拟”。文章分为四类模拟研究。七个MeSH术语的分配(模拟训练，患者模拟,高保真模拟训练,计算机模拟,患者特定模型，虚拟现实,和虚拟现实暴露疗法)进行了研究。准确性指标(灵敏度，精度,或阳性预测值)计算每个类别的研究。
结果：从53种不同的单词组合中获得了一组7213篇文章，2634被排除为无关紧要。“模拟患者”和“标准化/标准化患者”是最常用的术语。4579条包括文章，发表在1044种不同的期刊上，被分类为：“机器/自动化”（8.6%），“教育”（75.9%）和“实践审计”（11.4%）；4.1%为“不清楚”。文章的索引中位数为10MeSH(IQR8-13)；然而，45.5%的人没有使用七个MeSH术语中的任何一个进行索引。患者模拟是最普遍的MeSH（24.0%）。自动化文章与计算机模拟MeSH更相关（灵敏度=54.5%；精度=25.1%），而教育文章与患者模拟MeSH相关（灵敏度=40.2%；精度=80.9%）。实践审核文章也被极化为患者模拟MeSH（灵敏度=34.6％；精度=10.5％）。
结论：观察到与患者模拟相关的自由文本单词的使用不一致，以及基于人类的MeSH分配中的不准确性。这些限制可能会损害相关文献检索以支持证据综合练习。
