BACKGROUND: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed.
METHODS: A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters\' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated.
RESULTS: Three search filters were created in MEDLINE: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions.
CONCLUSIONS: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

BACKGROUND: Continual evidence surveillance is an integral feature of living guidelines. The Australian Stroke Guidelines include recommendations on 100 clinical topics and have been \'living\' since 2018.
OBJECTIVE: To describe the approach for establishing and evaluating an evidence surveillance system for the living Australian Stroke Guidelines.
METHODS: We developed a pragmatic surveillance system based on an analysis of the searches for the 2017 Stroke Guidelines and evaluated its reliability by assessing the potential impact on guideline recommendations. Search retrieval and screening workload are monitored monthly, together with the frequency of changes to the guideline recommendations.
RESULTS: Evidence surveillance was guided by practical considerations of efficiency and sustainability. A single PubMed search covering all guideline topics, limited to systematic reviews and randomised trials, is run monthly. The search retrieves about 400 records a month of which a sixth are triaged to the guideline panels for further consideration. Evaluations with Epistemonikos and the Cochrane Stroke Trials Register demonstrated the robustness of adopting this more restrictive approach. Collaborating with the guideline team in designing, implementing and evaluating the surveillance is essential for optimising the approach.
CONCLUSIONS: Monthly evidence surveillance for a large living guideline is feasible and sustainable when applying a pragmatic approach.

To present strategies for managing tumor mass formation and their corresponding postoperative outcomes.
We conducted a systematic literature review following the guidelines and protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched the PubMed and EMBASE databases, screened titles and abstracts, and further evaluated full-text publications to select relevant studies. Additionally, a narrative review of other pertinent articles on PubMed was performed. Case reports, cohort studies, and clinical trials were included. Animal studies were excluded.
Of 6 patients enrolled in this study, most had American Spinal Injury Association Impairment Scale grade A (66.7%) following intramedullary injury, and 1 patient had American Spinal Injury Association Impairment Scale grade D (16.65%). The discovery time of the intramedullary mass formation ranged from approximately 5 to 14 years. Surgical intervention was performed in most cases (66.7%), with improvement reported in 3 of the surgical cases (75%). The majority of cases (83.3%) involved cervical lesions, while only 1 case (16.7%) involved a thoracic lesion.
Due to the scarcity of described cases, there is no specific treatment for this tumor. Although our patient remained stable after conservative treatment, other studies have shown improvement in symptoms after mass resection. It is essential that the management of this complication be researched further due to the variety of clinical characteristics presented.

This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.

The studies on bibliometric analyses of case reports usually give valuable information regarding various aspects of case reports but lack investigation analysing these publications. This is the first-ever study to examine the bibliometric articles on case reports; hence, it is hypothesized to provide a valuable contribution to this gap. PubMed and SCOPUS databases were searched, and a total of 119 articles were obtained, but only five were analyzed matching the inclusion criteria. The keywords involved in the search were \"Bibliometrics\", \"analysis\", \"case reports\", \"case series\", and \"articles\" whereas, the time range in which the case reports were searched for was between 2011-2021. Common parameters from these five articles were employed for bibliometric analysis, which included publication year, publication type, the number of case reports per article, theme or subject of the article, citation, and impact factor (IF). Out of the five articles identified, four were published in 2021. One out of five was a case report, and the rest were review-type of articles. The overall citation number of these articles was less than 10, and the IF of these articles was between 0-0.007. The number of citations of the articles was in a period of one to two years or less than one year. A comprehensive overview of the parametrises, as well as the recent trends that are being used to conduct bibliometric analysis on case reports was acquired.

Text mining has been shown to be an auxiliary but key driver for modeling, data harmonization, and interpretation in bio-medicine. Scientific literature holds a wealth of information and embodies cumulative knowledge and remains the core basis on which mechanistic pathways, molecular databases, and models are built and refined. Text mining provides the necessary tools to automatically harness the potential of text. In this study, we show the potential of large-scale text mining for deriving novel insights, with a focus on the growing field of microbiome. We first collected the complete set of abstracts relevant to the microbiome from PubMed and used our text mining and intelligence platform Taxila for analysis. We drive the usefulness of text mining using two case studies. First, we analyze the geographical distribution of research and study locations for the field of microbiome by extracting geo mentions from text. Using this analysis, we were able to draw useful insights on the state of research in microbiome w. r.t geographical distributions and economic drivers. Next, to understand the relationships between diseases, microbiome, and food which are central to the field, we construct semantic relationship networks between these different concepts central to the field of microbiome. We show how such networks can be useful to derive useful insight with no prior knowledge encoded.

Meta-analyses aggregate results of different clinical studies to assess the effectiveness of a treatment. Despite their importance, meta-analyses are time-consuming and labor-intensive as they involve reading hundreds of research articles and extracting data. The number of research articles is increasing rapidly and most meta-analyses are outdated shortly after publication as new evidence has not been included. Automatic extraction of data from research articles can expedite the meta-analysis process and allow for automatic updates when new results become available. In this study, we propose a system for automatically extracting data from research abstracts and performing statistical analysis.
Our corpus consists of 1011 PubMed abstracts of breast cancer randomized controlled trials annotated with the core elements of clinical trials: Participants, Intervention, Control, and Outcomes (PICO). We proposed a BERT-based named entity recognition (NER) model to identify PICO information from research abstracts. After extracting the PICO information, we parse numeric outcomes to identify the number of patients having certain outcomes for statistical analysis.
The NER model extracted PICO elements with relatively high accuracy, achieving F1-scores greater than 0.80 in most entities. We assessed the performance of the proposed system by reproducing the results of an existing meta-analysis. The data extraction step achieved high accuracy, however the statistical analysis step achieved low performance because abstracts sometimes lack all the required information.
We proposed a system for automatically extracting data from research abstracts and performing statistical analysis. We evaluated the performance of the system by reproducing an existing meta-analysis and the system achieved a relatively good performance, though more substantiation is required.

Many clinical natural language processing methods rely on non-contextual word embedding (NCWE) or contextual word embedding (CWE) models. Yet, few, if any, intrinsic evaluation benchmarks exist comparing embedding representations against clinician judgment. We developed intrinsic evaluation tasks for embedding models using a corpus of radiology reports: term pair similarity for NCWEs and cloze task accuracy for CWEs. Using surveys, we quantified the agreement between clinician judgment and embedding model representations. We compare embedding models trained on a custom radiology report corpus (RRC), a general corpus, and PubMed and MIMIC-III corpora (P&MC). Cloze task accuracy was equivalent for RRC and P&MC models. For term pair similarity, P&MC-trained NCWEs outperformed all other NCWE models (ρspearman 0.61 vs. 0.27-0.44). Among models trained on RRC, fastText models often outperformed other NCWE models and spherical embeddings provided overly optimistic representations of term pair similarity.

Quantify tradeoffs in performance, reproducibility, and resource demands across several strategies for developing clinically relevant word embeddings.
We trained separate embeddings on all full-text manuscripts in the Pubmed Central (PMC) Open Access subset, case reports therein, the English Wikipedia corpus, the Medical Information Mart for Intensive Care (MIMIC) III dataset, and all notes in the University of Pennsylvania Health System (UPHS) electronic health record. We tested embeddings in six clinically relevant tasks including mortality prediction and de-identification, and assessed performance using the scaled Brier score (SBS) and the proportion of notes successfully de-identified, respectively.
Embeddings from UPHS notes best predicted mortality (SBS 0.30, 95% CI 0.15 to 0.45) while Wikipedia embeddings performed worst (SBS 0.12, 95% CI -0.05 to 0.28). Wikipedia embeddings most consistently (78% of notes) and the full PMC corpus embeddings least consistently (48%) de-identified notes. Across all six tasks, the full PMC corpus demonstrated the most consistent performance, and the Wikipedia corpus the least. Corpus size ranged from 49 million tokens (PMC case reports) to 10 billion (UPHS).
Embeddings trained on published case reports performed as least as well as embeddings trained on other corpora in most tasks, and clinical corpora consistently outperformed non-clinical corpora. No single corpus produced a strictly dominant set of embeddings across all tasks and so the optimal training corpus depends on intended use.
Embeddings trained on published case reports performed comparably on most clinical tasks to embeddings trained on larger corpora. Open access corpora allow training of clinically relevant, effective, and reproducible embeddings.

UNASSIGNED: A growing volume of studies address methods for performing systematic reviews of qualitative studies. One such methodological aspect is the conceptual framework used to structure the review question and plan the search strategy for locating relevant studies. The purpose of this case study was to evaluate the retrieval potential of each element of conceptual frameworks in qualitative systematic reviews in the health sciences.
UNASSIGNED: The presence of elements from conceptual frameworks in publication titles, abstracts, and controlled vocabulary in CINAHL and PubMed was analyzed using a set of qualitative reviews and their included studies as a gold standard. Using a sample of 101 publications, we determined whether particular publications could be retrieved if a specific element from the conceptual framework was used in the search strategy.
UNASSIGNED: We found that the relative recall of conceptual framework elements varied considerably, with higher recall for patient/population (99%) and research type (97%) and lower recall for intervention/phenomenon of interest (74%), outcome (79%), and context (61%).
UNASSIGNED: The use of patient/population and research type elements had high relative recall for qualitative studies. However, other elements should be used with great care due to lower relative recall.