Postgraduate Programme in Pharmaceutical Sciences, Federal University of Parana, 80210-170 Curitiba, Brazil.
Doctoral Programme in Pharmaceutical Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.
Int J Pharm Pract. 2024 Sep 3;32(5):396-404. doi: 10.1093/ijpp/riae042.
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.
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.
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%).
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.
评估以人类为基础的医学主题词（MeSH）在关于“患者模拟”的文章中的分配情况，患者模拟是一种使用受控患者反应模拟真实患者场景的技术。
创建了一个在 Medical Text Indexer-Auto 实施之前（2019 年）索引的验证集，其中包含 150 个可能涉及“患者模拟”的组合。文章被分为四类模拟研究。研究了七个 MeSH 术语（模拟培训、患者模拟、高保真模拟培训、计算机模拟、患者特定建模、虚拟现实和虚拟现实暴露疗法）的分配情况。为每个研究类别计算了准确度指标（灵敏度、精度或阳性预测值）。
从 53 个不同的词组合中获得了一组 7213 篇文章，其中 2634 篇被排除为不相关。“模拟患者”和“标准化/标准化患者”是使用最多的术语。包含的 4579 篇文章发表在 1044 种不同的期刊上，分为：“机器/自动化”（8.6%）、“教育”（75.9%）和“实践审计”（11.4%）；4.1%为“不清楚”。文章的平均索引数为 10 个 MeSH（IQR 8-13）；然而，45.5%的文章没有索引到七个 MeSH 术语中的任何一个。患者模拟是最常见的 MeSH（24.0%）。自动化文章与计算机模拟 MeSH 的相关性更高（灵敏度=54.5%；精度=25.1%），而教育文章与患者模拟 MeSH 的相关性更高（灵敏度=40.2%；精度=80.9%）。实践审计文章也倾向于患者模拟 MeSH（灵敏度=34.6%；精度=10.5%）。
观察到与患者模拟相关的自由文本词的使用不一致，以及人为的 MeSH 分配不准确。这些限制可能会影响相关文献检索，从而影响证据综合的效果。
