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.

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.

Time-limited eating is a dietary intervention whereby eating is limited to a specific window of time during the day. The usual eating windows of adults, and how these can be manipulated for dietary interventions, is well documented. However, there is a paucity of data on eating windows of young people, the manipulation of which may be a useful intervention for reducing obesity. This paper reviewed the existing literature on the eating windows of children and adolescents, aged 5-18 years, plus clock times of first and last intakes and variations by subgroup. Two databases (Medline and Embase) were searched for eligible papers published between February 2013 and February 2023, with forward searching of the citation network of included studies on Web of Science. Articles were screened, and data extracted, in duplicate by two independent reviewers. Ten studies were included, with both observational and experimental designs. Narrative synthesis showed large variations in eating windows with average values ranging from 9.7 h to 16.4 h. Meta-analysis, of five studies, showed a pooled mean daily eating window of 11.3 h (95% CI 11.0, 11.7). Large variations in eating windows exist across different study populations; however, the pooled data suggest that it may be possible to design time-limited eating interventions in paediatric populations aimed at reducing eating windows. Further high-quality research, investigating eating windows and subsequent associations with health outcomes, is needed.

OBJECTIVE: Septic arthritis (SA) of the native knee joint is associated with significant morbidity. This review compared post-operative functional outcomes (patient-reported outcome measures (PROMs) and range of movement (ROM)) following arthroscopic washout (AW) and open washout (OW) amongst adult patients with SA of the native knee. The need for further operative intervention was also considered.
METHODS: Electronic databases of PubMed, MEDLINE, Embase, Cochrane, Web of Science and Scopus were searched between 16 February 2023 and 18 March 2023. Randomised controlled trials (RCTs) and comparative observational analytic studies comparing function (reflected in PROMs or ROM) at latest follow-up following AW and OW were included. A narrative summary was provided concerning post-operative PROMs. Pooled estimates for mean ROM and re-operation rates were conducted using the random-effects model. The risk of bias was assessed using the Cochrane risk-of-bias assessment tool-2 for RCTs and the Risk of Bias in Non-Randomized Studies of Interventions tool for observational analytic studies.
RESULTS: Of 2580 retrieved citations, 7 articles (1 RCT and 6 cohort studies) met the inclusion criteria. Of these, five had some concerns/moderate risk of bias, and two had serious risk. There was a slight tendency for superior mean PROMs following AW compared with OW, but due to small effect sizes, this was unlikely clinically relevant. Additionally, the use of four different PROMs scales made direct comparisons impossible. AW was associated with superior ROM (mean difference 20.18° (95% CI 14.35, 26.02; p < 0.00001)), whilst there was a tendency for lower re-operation requirements following AW (OR 0.64, 95% CI 0.26, 1.57, p = 0.44).
CONCLUSIONS: AW was associated with equivalent to superior post-operative function and lower requirement for further intervention compared with OW. Results need to be interpreted cautiously, taking into consideration the methodological and clinical heterogeneity of the included studies.
BACKGROUND: PROSPERO 2022, CRD42022364062.

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.

The seasonal outbreaks of Mpox continue in most parts of West and Central Africa. In the past year, Nigeria had the highest number of reported cases. Here, we used the PRISMA guidelines to carry out a systematic review and meta-analysis of available evidence on Mpox in Nigeria to assess the prevalence, transmission pattern, diagnostic approach, and other associated factors useful for mitigating the transmission of the disease. All relevant observational studies in PubMed/MEDLINE, Embase, AJOL, Web of Science, Scopus and Google Scholar on Mpox in Nigeria were assessed within the last fifty years (1972 to 2022). In all, 92 relevant articles were retrieved, out of which 23 were included in the final qualitative analysis. Notably, most of the cases of Mpox in Nigeria were from the southern part of the country. Our findings showed a progressive spread from the southern to the northern region of the country. We identified the following factors as important in the transmission of Mpox in Nigeria; poverty, lack of basic healthcare facilities, and risk of exposure through unsafe sexual practices. Our findings reiterate the need to strengthen and expand existing efforts as well as establish robust multi-sectoral collaboration to understand the dynamics of Mpox Nigeria.

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).