BACKGROUND: The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice.
METHODS: In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies.
RESULTS: From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs.
CONCLUSIONS: Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

OBJECTIVE: Periodically, medical publications are retracted. The reasons vary from minor situations, such as author attributions, which do not undermine the validity of the data or the analysis in the article, to serious reasons, such as fraud. Understanding the reasons for retraction can provide important information for clinicians, educators, researchers, journals, and editorial boards.
METHODS: The PubMed database was searched using the term \"COVID-19\" (coronavirus disease 2019) and the term limitation \"retracted publication.\" The characteristics of the journals with retracted articles, the types of article, and the reasons for retraction were analyzed.
RESULTS: This search recovered 196 articles that had been retracted. These retractions were published in 179 different journals; 14 journals had >1 retracted article. The mean impact factor of these journals was 8.4, with a range of 0.32-168.9. The most frequent reasons for retractions were duplicate publication, concerns about data validity and analysis, concerns about peer review, author request, and the lack of permission or ethical violation. There were significant differences between the types of article and the reasons for retraction but no consistent pattern. A more detailed analysis of two particular retractions demonstrates the complexity and the effort required to make decisions about article retractions.
CONCLUSIONS: The retraction of published articles presents a significant challenge to journals, editorial boards, peer reviewers, and authors. This process has the potential to provide important benefits; it also has the potential to undermine confidence in both research and the editorial process.

UNASSIGNED: After conducting a comprehensive literature search of two medical electronic databases, PubMed and Embase, as well as two citation databases, Web of Science Core Collections (WoS) and Scopus, we aimed to conduct an Altmetric and Scientometric analysis of the History of Medicine literature in medical research.
UNASSIGNED: The following software tools were used for analyzing the retrieved records from PubMed and Embase databases and conducting a collaboration analysis to identify the countries involved in scientific medical papers, as well as clustering keywords to reveal the trend of History of Medicine research for the future. These software tools (VOSviewer 1.6.18 and Spss 16) allowed the researchers to visualize bibliometric networks, perform statistical analysis, and identify patterns and trends in the data.
UNASSIGNED: Our analysis revealed 53,771 records from PubMed and 54,405 records from EMBASE databases retrieved in the field of History of Medicine by 105,286 contributed authors in WoS. We identified 157 countries that collaborated on scientific medical papers. By clustering 59,995 keywords, we were able to reveal the trend of History of Medicine research for the future. Our findings showed a positive association between traditional bibliometrics and social media metrics such as the Altmetric Attention Score in the History of Medicine literature (p < 0.05).
UNASSIGNED: Sharing research findings of articles in social scientific networks will increase the visibility of scientific works in History of Medicine research, which is one of the most important factors influencing the citation of articles. Additionally, our overview of the literature in the medical field allowed us to identify and examine gaps in the History of Medicine research.

Using two databases, this bibliometric analysis was done for the papers published by the Faculty of Medicine, University of Khartoum (FMUK), from 2019 to 2023. Data were extracted from SCImago for all Sudan, and from PubMed for the publications by FMUK and its associated research centres, the Institute of Endemic Diseases, and the Mycetoma Research Center. The analysis of publications included the count and type of publications, the journals, and national and international collaboration assessment. The publications from FMUK show improvement over time in number and quality, a growth that is significantly influenced by national and international collaboration. These partnerships have proven to be a key driver of FMUK\'s research output, together with the valuable contributions of the specialized research institutions. However, there is room for improvement in the research output by increasing institutional capacity to support research and scientific communication. The Sudanese Journal of Paediatrics is an example where open access has a positive impact by allowing peripheral journals to be established despite the constraints.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X\'s predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user\'s web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

BACKGROUND: Large language models (LLMs) that can efficiently screen and identify studies meeting specific criteria would streamline literature reviews. Additionally, those capable of extracting data from publications would enhance knowledge discovery by reducing the burden on human reviewers.
METHODS: We created an automated pipeline utilizing OpenAI GPT-4 32 K API version \"2023-05-15\" to evaluate the accuracy of the LLM GPT-4 responses to queries about published papers on HIV drug resistance (HIVDR) with and without an instruction sheet. The instruction sheet contained specialized knowledge designed to assist a person trying to answer questions about an HIVDR paper. We designed 60 questions pertaining to HIVDR and created markdown versions of 60 published HIVDR papers in PubMed. We presented the 60 papers to GPT-4 in four configurations: (1) all 60 questions simultaneously; (2) all 60 questions simultaneously with the instruction sheet; (3) each of the 60 questions individually; and (4) each of the 60 questions individually with the instruction sheet.
RESULTS: GPT-4 achieved a mean accuracy of 86.9% - 24.0% higher than when the answers to papers were permuted. The overall recall and precision were 72.5% and 87.4%, respectively. The standard deviation of three replicates for the 60 questions ranged from 0 to 5.3% with a median of 1.2%. The instruction sheet did not significantly increase GPT-4\'s accuracy, recall, or precision. GPT-4 was more likely to provide false positive answers when the 60 questions were submitted individually compared to when they were submitted together.
CONCLUSIONS: GPT-4 reproducibly answered 3600 questions about 60 papers on HIVDR with moderately high accuracy, recall, and precision. The instruction sheet\'s failure to improve these metrics suggests that more sophisticated approaches are necessary. Either enhanced prompt engineering or finetuning an open-source model could further improve an LLM\'s ability to answer questions about highly specialized HIVDR papers.

UNASSIGNED: With exponential growth in the publication of interprofessional education (IPE) research studies, it has become more difficult to find relevant literature and stay abreast of the latest research. To address this gap, we developed, evaluated, and validated search strategies for IPE studies in PubMed, to improve future access to and synthesis of IPE research. These search strategies, or search hedges, provide comprehensive, validated sets of search terms for IPE publications.
UNASSIGNED: The search strategies were created for PubMed using relative recall methodology. The research methods followed the guidance of previous search hedge and search filter validation studies in creating a gold standard set of relevant references using systematic reviews, having expert searchers identify and test search terms, and using relative recall calculations to validate the searches\' performance against the gold standard set.
UNASSIGNED: The three recommended search hedges for IPE studies presented had recall of 71.5%, 82.7%, and 95.1%; the first more focused for efficient literature searching, the last with high recall for comprehensive literature searching, and the remaining hedge as a middle ground between the other two options.
UNASSIGNED: These validated search hedges can be used in PubMed to expedite finding relevant scholarships, staying up to date with IPE research, and conducting literature reviews and evidence syntheses.

UNASSIGNED: There is a need for additional comprehensive and validated filters to find relevant references more efficiently in the growing body of research on immigrant populations. Our goal was to create reliable search filters that direct librarians and researchers to pertinent studies indexed in PubMed about health topics specific to immigrant populations.
UNASSIGNED: We applied a systematic and multi-step process that combined information from expert input, authoritative sources, automation, and manual review of sources. We established a focused scope and eligibility criteria, which we used to create the development and validation sets. We formed a term ranking system that resulted in the creation of two filters: an immigrant-specific and an immigrant-sensitive search filter.
UNASSIGNED: When tested against the validation set, the specific filter sensitivity was 88.09%, specificity 97.26%, precision 97.88%, and the NNR 1.02. The sensitive filter sensitivity was 97.76%when tested against the development set. The sensitive filter had a sensitivity of 97.14%, specificity of 82.05%, precision of 88.59%, accuracy of 90.94%, and NNR [See Table 1] of 1.13 when tested against the validation set.
UNASSIGNED: We accomplished our goal of developing PubMed search filters to help researchers retrieve studies about immigrants. The specific and sensitive PubMed search filters give information professionals and researchers options to maximize the specificity and precision or increase the sensitivity of their search for relevant studies in PubMed. Both search filters generated strong performance measurements and can be used as-is, to capture a subset of immigrant-related literature, or adapted and revised to fit the unique research needs of specific project teams (e.g. remove US-centric language, add location-specific terminology, or expand the search strategy to include terms for the topic/s being investigated in the immigrant population identified by the filter). There is also a potential for teams to employ the search filter development process described here for their own topics and use.

From its first printing in 1879 to when publication ceased in 2004, the Index Medicus had proved invaluable for persons wishing to conduct healthcare-related research. With the loss of this resource and the rapid expansion of alternative, online sources, it is vital that persons understand how to appropriately search for and use this information. The purpose of this review is to outline the information sources available, discuss how to use current search technology to best obtain relevant information while minimizing nonproductive references, and give the author\'s opinion on the reliability of the various informational sources available. Topics to be discussed will include Medical Subject Headings and PICO searches and sources ranging from the National Library of Medicine and Cochrane Reviews to Wikipedia and other sites, such as associations and commercial interest sites.

OBJECTIVE: The COVID-19 pandemic precipitated a significant transformation of scientific journals. Our aim was to determine how critical care (CC) journals and their impact may have evolved during the COVID-19 pandemic. We hypothesized that the impact, as measured by citations and publications, from the field of CC would increase.
METHODS: Observational study of journal publications, citations, and retractions status.
METHODS: All work was done electronically and retrospectively.
METHODS: The top 18 CC journals broadly concerning CC, and the top 5 most productive CC journals on the SCImago list.
METHODS: None.
RESULTS: For the top 18 CC journals and specifically Critical Care Medicine (CCM), time series analysis was used to estimate the trends of total citations, citations per publication, and publications per year by using the best-fit curve. We used PubMed and Retraction Watch to determine the number of COVID-19 publications and retractions. The average total citations and citations per publication for all journals was an upward quadratic trend with inflection points in 2020, whereas publications per year spiked in 2020 before returning to prepandemic values in 2021. For CCM total publications trend downward while total citations and citations per publication generally trend up from 2017 onward. CCM had the lowest percentage of COVID-related publications (15.7%) during the pandemic and no reported retractions. Two COVID-19 retractions were noted in our top five journals.
CONCLUSIONS: Citation activity across top CC journals underwent a dramatic increase during the COVID-19 pandemic without significant retraction data. These trends suggest that the impact of CC has grown significantly since the onset of COVID-19 while maintaining adherence to a high-quality peer-review process.