Evaluation of Medical Subject Headings assignment in simulated patient articles.

Objectives: 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 implementati...

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Published in:International Journal of Pharmacy Practice Vol. 32; no. 5; pp. 396 - 405
Main Authors: Tonin, Fernanda S, Negrão, Luciana G, Meza, Isabela P, Fernandez-Llimos, Fernando
Format: equations & formulas research tables/charts Journal Article
Published: Oxford University Press / USA Oct2024
Online Access:View this record in EBSCOhost
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      dt: Oct2024
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      pub: Oxford University Press / USA
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        atl: Evaluation of Medical Subject Headings assignment in simulated patient articles.
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          Tonin, Fernanda S
          Negrão, Luciana G
          Meza, Isabela P
          Fernandez-Llimos, Fernando
        affil: Postgraduate Programme in Pharmaceutical Sciences, Federal University of Parana, 80210-170 Curitiba, Brazil
      sug:
        subj:
          National Library of Medicine (U.S.)
          Subject Headings
          Computer Simulation
          Patient Simulation
          Abstracting and Indexing
          Human
          Retrospective Design
          Record Review
          Vocabulary, Controlled
          PubMed
          Cost Effectiveness Analysis
          Random Sample
          Descriptive Statistics
          Data Analysis Software
          kappa Statistic
          Virtual Reality
          Virtual Reality Exposure Therapy
          Sensitivity and Specificity
          Automation
          Literature Searching
      ab: Objectives: 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. Key findings: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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