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.

UNASSIGNED: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases.
UNASSIGNED: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020.
UNASSIGNED: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases.

OBJECTIVE: Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names.
METHODS: The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE\'s author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation.
RESULTS: Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN.
CONCLUSIONS: The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

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.

Surgical site infections (SSIs) pose a significant clinical challenge, with heightened risks and severe consequences for diabetic patients undergoing surgical procedures. This systematic review aims to synthesize the current evidence on effective prevention strategies for mitigating SSI risk in this vulnerable population. From inception to March 2024, we comprehensively searched multiple electronic databases (PubMed, Medline, Embase, Cochrane Library, CINAHL) to identify relevant studies evaluating SSI prevention strategies in diabetic surgical patients. Our search strategy followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, utilizing a combination of keywords and Medical Subject Headings (MeSH) terms related to diabetes, surgical site infections, prevention strategies, and surgical procedures. Inclusion criteria focused on peer-reviewed clinical trials, randomized controlled trials, and meta-analyses published in English. The search yielded three studies meeting the eligibility criteria, subject to data extraction and qualitative synthesis. Key findings highlighted the efficacy of interventions such as optimized perioperative glycemic control, timely prophylactic antibiotic administration, and meticulous preoperative skin antisepsis in reducing SSI rates among diabetic surgical patients. The potential for personalized prevention approaches based on individual patient factors, such as diabetes type and surgical complexity, was explored. This systematic review underscores the importance of a multifaceted, evidence-based approach to SSI prevention in diabetic surgical patients, integrating strategies like glycemic control, antibiotic prophylaxis, and preoperative skin antisepsis. Furthermore, our findings suggest the potential benefits of personalized care pathways tailored to individual patient characteristics. Implementing these interventions requires interdisciplinary collaboration, adaptation to diverse healthcare settings, and patient engagement through culturally sensitive education initiatives. This comprehensive analysis informs clinical practice, fosters patient safety, and contributes to the global efforts to enhance surgical outcomes for this high-risk population.

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB.
METHODS: We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts.
RESULTS: Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct.
CONCLUSIONS: These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.

BACKGROUND: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.
RESULTS: Word embeddings, which represent a word\'s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug\'s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.
CONCLUSIONS: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique\'s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

BACKGROUND: Palliative care in low- or middle-income country (LMIC) humanitarian settings is a new area, experiencing a degree of increased momentum over recent years. The review contributes to this growing body of knowledge, in addition to identifying gaps for future research. The overall aim is to systematically explore the evidence on palliative care needs of patients and/or their families in LMIC humanitarian settings.
METHODS: Arksey and O\'Malley\'s (Int J Soc Res Methodol. 8:19-32, 2005) scoping review framework forms the basis of the study design, following further guidance from Levac et al. (Implement Sci 5:1-9, 2010), the Joanna Briggs Institute (JBI) Peters et al. (JBI Reviewer\'s Manual JBI: 406-452, 2020), and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) from Tricco et al. (Ann Intern Med 169:467-73, 2018). This incorporates a five-step approach and the population, concept, and context (PCC) framework. Using already identified key words/terms, searches for both published research and gray literature from January 2012 to October 2022 will be undertaken using databases (likely to include Cumulative Index of Nursing and Allied Health (CINAHL), MEDLINE, Embase, Global Health, Scopus, Applied Social Science Index and Abstracts (ASSIA), Web of Science, Policy Commons, JSTOR, Library Network International Monetary Fund and World Bank, Google Advanced Search, and Google Scholar) in addition to selected pre-print sites and websites. Data selection will be undertaken based on the inclusion and exclusion criteria and will be reviewed at each stage by two reviewers, with a third to resolve any differences. Extracted data will be charted in a table. Ethical approval is not required for this review.
CONCLUSIONS: Findings will be presented in tables and diagrams/charts, followed by a narrative description. The review will run from late October 2022 to early 2023. This is the first systematic scoping review specifically exploring the palliative care needs of patients and/or their family, in LMIC humanitarian settings. The paper from the review findings will be submitted for publication in 2023.

OBJECTIVE: It is essential for health researchers to have a systematic understanding of third-party variables that influence both the exposure and outcome under investigation, as shown by a directed acyclic graph (DAG). The traditional construction of DAGs through literature review and expert knowledge often needs to be more systematic and consistent, leading to potential biases. We try to introduce an automatic approach to building network linking variables of interest.
METHODS: Large-scale text mining from medical literature was utilized to construct a conceptual network based on the Semantic MEDLINE Database (SemMedDB). SemMedDB is a PubMed-scale repository of the \"concept-relation-concept\" triple format. Relations between concepts are categorized as Excitatory, Inhibitory, or General.
RESULTS: To facilitate the use of large-scale triple sets in SemMedDB, we have developed a computable biomedical knowledge (CBK) system (https://cbk.bjmu.edu.cn/), a website that enables direct retrieval of related publications and their corresponding triples without the necessity of writing SQL statements. Three case studies were elaborated to demonstrate the applications of the CBK system.
CONCLUSIONS: The CBK system is openly available and user-friendly for rapidly capturing a set of influencing factors for a phenotype and building candidate DAGs between exposure-outcome variables. It could be a valuable tool to reduce the exploration time in considering relationships between variables, and constructing a DAG. A reliable and standardized DAG could significantly improve the design and interpretation of observational health research.

BACKGROUND: Deep brain stimulation (DBS) can be used to treat several neurological and psychiatric conditions such as Parkinson\'s disease, epilepsy and obsessive-compulsive disorder; however, limited work has been done to assess the disparities in DBS access and implementation. The goal of this scoping review is to identify sources of disparity in the clinical provision of DBS.
METHODS: A scoping review will be conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-extension for Scoping Reviews methodology. Relevant studies will be identified from databases including MEDLINE/PubMed, EMBASE and Web of Science, as well as reference lists from retained articles. Initial search dates were in January 2023, with the study still ongoing. An initial screening of the titles and abstracts of potentially eligible studies will be completed, with relevant studies collected for full-text review. The principal investigators and coauthors will then independently review all full-text articles meeting the inclusion criteria. Data will be extracted and collected in table format. Finally, results will be synthesised in a table and narrative report.
BACKGROUND: No institutional board review or approval is necessary for the proposed scoping review. The findings will be submitted for publication to relevant peer-reviewed journals and conferences.
UNASSIGNED: This protocol has been registered prospectively on the Open Science Framework (https://osf.io/cxvhu).