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.

OBJECTIVE: To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH).
METHODS: High priority SDoH terms and definitions were collated from authoritative sources, curated based on publication frequencies, and refined by subject matter experts. Descriptive analyses were used to investigate how PubMed search details and best match results were affected by the addition of SDoH concepts to MeSH. Three information retrieval metrics (Precision, Recall, and F measure) were used to quantitatively assess the accuracy of PubMed search results. Pre- and post-update documents were clustered into topic areas using a Natural Language Processing pipeline, and SDoH relevancy assessed.
RESULTS: Addition of 35 SDoH terms to MeSH resulted in more accurate algorithmic translations of search terms and more reliable best match results. The Precision, Recall, and F measures of post-update results were significantly higher than those of pre-update results. The percentage of retrieved publications belonging to SDoH clusters was significantly greater in the post- than pre-update searches.
CONCLUSIONS: This evaluation confirms that inclusion of new SDoH terms in MeSH can lead to qualitative and quantitative enhancements in PubMed search retrievals. It demonstrates the methodology for and impact of suggesting new terms for MeSH indexing. It provides a foundation for future efforts across behavioral and social science research (BSSR) domains.
CONCLUSIONS: Improving the representation of BSSR terminology in MeSH can improve PubMed search results, thereby enhancing the ability of investigators and clinicians to build and utilize a cumulative BSSR knowledge base.

OBJECTIVE: This investigation aims to analyze the characteristics and development of literature and advocate to include \"Somatopsychic\" as a Medical Subject Headings (MeSH) term.
BACKGROUND: The interplay between physiological processes and psychological conditions, commonly referred to as \"Somatopsychic,\" has garnered increasing attention in scientific literature over the years.
METHODS: Somatopsychic-related research from the Scopus database using (Text word) and (MeSH) features. Publications were collected on Mar 22, 2023. The publication output was then analyzed using the R package\'s bibliometrics (Biblioshiny) and VOSviewer.
RESULTS: In this study, search results for \"somatopsychic\" using (MeSH) resulted in a predictable return of 0 articles. Meanwhile, based on a search with (Text word), this study retrieved 306 documents for an unlimited period (and yielded published articles between 1913 and 2022). The analysis also revealed that 3,176 authors contributed to publications related to somatopsychic, with the United States ranking first in terms of authorship. In addition, the study presented a co-word network that illustrated frequent co-occurrence of particular keywords within somatopsychic research.
CONCLUSIONS: This study reveals that somatopsychic-related publications are becoming increasingly prevalent. Adding somatopsychic as a dedicated term to the MeSH thesaurus of the National Library of Medicine would assist in indexing and retrieving the most pertinent literature on this topic (Tab. 3, Fig. 5, Ref. 51).

BACKGROUND: The Medical Subject Headings (MeSH) thesaurus is the controlled vocabulary used to index articles in MEDLINE. MeSH were mainly manually selected until June 2022 when an automated algorithm, the Medical Text Indexer (MTI) automated was fully implemented. A selection of automated indexed articles is then reviewed (curated) by human indexers to ensure the quality of the process.
OBJECTIVE: To describe the association of MEDLINE indexing methods (i.e., manual, automated, and automated + curated) on the MeSH assignment in pharmacy practice journals compared with medical journals.
METHODS: Original research articles published between 2016 and 2023 in two groups of journals (i.e., the Big-five general medicine and three pharmacy practice journals) were selected from PubMed using journal-specific search strategies. Metadata of the articles, including MeSH terms and indexing method, was extracted. A list of pharmacy-specific MeSH terms had been compiled from previously published studies, and their presence in pharmacy practice journal records was investigated. Using bivariate and multivariate analyses, as well as effect size measures, the number of MeSH per article was compared between journal groups, geographic origin of the journal, and indexing method.
RESULTS: A total of 8479 original research articles was retrieved: 6254 from the medical journals and 2225 from pharmacy practice journals. The number of articles indexed by the various methods was disproportionate; 77.8 % of medical and 50.5 % of pharmacy manually indexed. Among those indexed using the automated system, 51.1 % medical and 10.9 % pharmacy practice articles were then curated to ensure the indexing quality. Number of MeSH per article varied among the three indexing methods for medical and pharmacy journals, with 15.5 vs. 13.0 in manually indexed, 9.4 vs. 7.4 in automated indexed, and 12.1 vs. 7.8 in automated and then curated, respectively. Multivariate analysis showed significant effect of indexing method and journal group in the number of MeSH attributed, but not the geographical origin of the journal.
CONCLUSIONS: Articles indexed using automated MTI have less MeSH than manually indexed articles. Articles published in pharmacy practice journals were indexed with fewer number of MeSH compared with general medical journal articles regardless of the indexing method used.

The objective of this study was to examine the accuracy of indexing for \"Appalachian Region\"[Mesh]. Researchers performed a search in PubMed for articles published in 2019 using \"Appalachian Region\"[Mesh] or \"Appalachia\" or \"Appalachian\" in the title or abstract. Only 17.88% of the articles retrieved by the search were about Appalachia according to the ARC definition. Most articles retrieved appeared because they were indexed with state terms that were included as part of the mesh term. Database indexing and searching transparency is of growing importance as indexers rely increasingly on automated systems to catalog information and publications.

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the U.S. National Library of Medicine (NLM) for classifying journal articles. It is increasingly used by researchers studying medical innovation to classify text into disease areas and other categories. Although this process was once manual, human indexers are now assisted by algorithms that automate some of the indexing process. NLM has made one of their algorithms, the Medical Text Indexer (MTI), available to researchers. MTI can be used to easily assign MeSH descriptors to arbitrary text, including from document types other than publications. However, the reliability of extending MTI to other document types has not been studied directly. To assess this, we collected text from grants, patents, and drug indications, and compared MTI\'s classification to expert manual classification of the same documents. We examined MTI\'s recall (how often correct terms were identified) and found that MTI identified 78% of expert-classified MeSH descriptors for grants, 78% for patents, and 86% for drug indications. This high recall could be driven merely by excess suggestions (at an extreme, all diseases being assigned to a piece of text); therefore, we also examined precision (how often identified terms were correct) and found that most MTI outputs were also identified by expert manual classification: precision was 53% for grant text, 73% for patent text, and 64% for drug indications. Additionally, we found that recall and precision could be improved by (i) utilizing ranking scores provided by MTI, (ii) excluding long documents, and (iii) aggregating to higher MeSH categories. For simply detecting the presence of any disease, MTI showed > 94% recall and > 87% precision. Our overall assessment is that MTI is a potentially useful tool for researchers wishing to classify texts from a variety of sources into disease areas.

BACKGROUND: Systems fragmentation is a major challenge for an efficient organization, integration being a potential solution also proposed in health care field, including pharmacy as a player. However, the use of different terms and definitions in the literature hinders the comparison of different integration initiatives.
OBJECTIVE: To identify and map the terms used in scientific literature regarding integration in health care and to characterize each emerging topic.
METHODS: A lexicographic analysis of the integration of healthcare systems literature indexed in PubMed was conducted. Ten different systematic searches, four using only Medical Subject Headings (MeSH) and six using text words, were conducted in March 2023. Journal scattering was analyzed following Bradford\'s distribution using the Leimkuhler model. An overall text corpus was created with titles and abstracts of all the records retrieved. The corpus was lemmatized, and the most used bigrams were tokenized as single strings. To perform a topic modeling, the lemmatized corpus text was analyzed using IRaMuTeQ, producing descending hierarchic classification and a correspondence analysis. The 50 words with higher chi-square statistics in each class were considered as representative of the class.
RESULTS: A total of 42,479 articles published from 1943 to 2023 in 4469 different journals were retrieved. The MeSH \"Delivery of Health Care, Integrated\", created in the 1996 MeSH update, was the most productive retrieving 33.7 % of the total articles but also retrieving 22.6 % of articles not retrieved in any other search. The text word \"Integration\" appeared in 15,357 (36.2 %) records. The lexicographic analysis resulted in 7 classes, named as: Evidence and implementation, Quantitative research, Professional education, Qualitative research, Governance and leadership, Clinical research, and Financial resources. Association between the classes and the searches or the text-words used ranged from moderate to weak demonstrating the lack of a standard pattern of use of terms in literature regarding healthcare integration.
CONCLUSIONS: The term \"integration\" and the MeSH \"Delivery of Health Care, Integrated\" are the most used to represent the concept of integration in healthcare and should be the preferred terms in the literature.

BACKGROUND: There are various Medical Subject Headings (MeSH) terms used to index general practice research, without consistency.
OBJECTIVE: To understand how general practice-related research is indexed in the main general practice journals between 2011 and 2021, and to analyse the factors that influenced the choice of the general practice-related MeSH.
METHODS: This was a quantitative bibliometric study conducted on MEDLINE.
METHODS: MeSH were selected according to the international definition of General Practice/Family Medicine: \'General Practice\', \'Primary Health Care\', \'Family Practice\', \'General Practitioners\', \'Physicians, Primary Care\', and \'Physicians, Family\'. Their use was studied from 2011 to 2021 on MEDLINE, reviewing the 20 general practice journals with the highest impact factors. A descriptive and analytical approach was used; the association of the country, journal, and year with the choice of general practice-related MeSH terms was analysed.
RESULTS: A total of 8514 of 150 286 articles (5.7%) were using one of the general practice-related MeSH terms. The most used were \'Primary Health Care\' (4648/9984, 46.6%) and \'General Practice\' (2841/9984, 28.5%). A total of 80.0% (6172/7723) of the articles were related to the UK or US and 71.0% (6055/8514) of the articles came from four journals (BJGP, BMJ, Journal of General Internal Medicine, and Annals of Family Medicine). Two main country clusters emerged from the use of general practice-related MeSH: a British cluster mainly using \'General Practice\' and an American cluster using \'Primary Health Care\'. The journals also mainly differed in their used of these two MeSH terms.
CONCLUSIONS: Important variations in the indexation of general practice research were found. Researchers should consider combining \'Primary Health Care\' and \'General Practice\' in their PubMed searches to access all the general practice research, regardless of their country of origin.

UNASSIGNED: Novel terms describing several designs of resin-bonded fixed partial dentures (RBFPDs) continue to appear. Indeed, a variety of terms are used in the English scientific literature The use of a standard terminology is important for a fair and efficient understanding. This study aimed to investigate if the terminology used to describe designs and retention methods for anterior RBFPDs is standard.
UNASSIGNED: An electronic search in the English literature was conducted in PubMed/Medline to identify all publications reporting RBFPDs in the anterior region until August 2022. This search was completed by hand searching. Terms indicating different designs of RBFPDs were listed and then classified. Percentages of their use were calculated to determine the commonly used terms. Analysis of the use of these terms was performed based on the standards determined by the latest edition of the Glossary of Prosthodontic Terms (GPT). The impacts of the MeSH Thesaurus and GPT on the nomenclature used for RBFPDs was assessed.
UNASSIGNED: A total of 125 articles were eligible for this review. In the retained articles, 86 terms were found. Among them, thirty-nine terms were classified into three groups. Only six terms were defined in the latest edition of GPT (GPT-9). Several classified terms that are commonly used were not identified in the GPT-9. Conversely to the GPT-9 which impact was insignificant, the MeSH Thesaurus had an important impact on the nomenclature used for RBFPDs.
UNASSIGNED: The terminology used to describe designs and retention methods for anterior RBFPDs was non-standard. The GPT-9, constituting an important reference, defined a limited number of terms related to RBFPDs and had no significant impact on the standardization of the terminology used for RBFPDs. Efforts should therefore be continued to standardize the terminology. A specialized mini-glossary grouping and defining all the terms found in this study will helpful in clarifying the terminology used for the anterior RBFPDs.

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis.
METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms).
RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%.
CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.