Co-design of patient information leaflets for germline predisposition to cancer: recommendations for clinical practice from the UK Cancer Genetics Group (UKCGG), Cancer Research UK (CRUK) funded CanGene-CanVar Programme and the Association of Genetic Nurse Counsellors (AGNC).

BACKGROUND: Testing for germline pathogenic variants (GPVs) in cancer predisposition genes is increasingly offered as part of routine care for patients with cancer. This is often urgent in oncology clinics due to potential implications on treatment and surgical decisions. This also allows identification of family members who should be offered predictive genetic testing. In the UK, it is common practice for healthcare professionals to provide a patient information leaflet (PIL) at point of care for diagnostic genetic testing in patients with cancer, after results disclosure when a GPV is identified, and for predictive testing of at-risk relatives. Services usually create their own PIL, resulting in duplication of effort and wide variability regarding format, content, signposting and patient input in co-design and evaluation. METHODS: Representatives from UK Cancer Genetics Group (UKCGG), Cancer Research UK (CRUK) funded CanGene-CanVar programme and Association of Genetic Nurse Counsellors (AGNC) held a 2-day meeting with the aim of making recommendations for clinical practice regarding co-design of PIL for germline cancer susceptibility genetic testing. Lynch syndrome and haematological malignancies were chosen as exemplar conditions. RESULTS: Meeting participants included patient representatives including as co-chair, multidisciplinary clinicians and other experts from across the UK. High-level consensus for UK recommendations for clinical practice was reached on several aspects of PIL using digital polling, including that PIL should be offered, accessible, co-designed and evaluated with patients. CONCLUSIONS: Recommendations from the meeting are likely to be applicable for PIL co-design for a wide range of germline genetic testing scenarios.

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

OBJECTIVE: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, prior studies have limited diversity. We aim to evaluate if there are racial/ethnic differences associated with ILD, time intervals between SSc and ILD and with emergency department (ED) visit or hospitalization rates. METHODS: Clinical and sociodemographic variables were extracted for 756 patients with SSc from longitudinal health records in an integrated health-system. Logistic regression models analyzed the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. RESULTS: Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.60; 95% confidence interval [CI], 1.29-5.28; p=0.008) compared with White patients. The predicted age in years of SSc-ILD was younger for Hispanic (estimate, -6.5; 95% CI, -13--0.21; p = 0.04) and Black/African American patients (-10; 95% CI -16--4.9; p < 0.001) compared with White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared with 20.6% of White patients), and the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47-4.49; p = 0.001) and Hispanic patients (HR 2.29; 95% CI 1.37- 3.82; p = 0.002) had higher rates of an ED visit. CONCLUSION: We found that odds of SSc-ILD differed by racial/ethnic group, minoritized patients had earlier age of presentation, and greater rates of an ED visit.

Pharmacogenomic testing and prescribing patterns for patients with cancer in a large national precision medicine cohort.

Population databases could help patients with cancer and providers better understand current pharmacogenomic prescribing and testing practices. This retrospective observational study analysed patients with cancer, drugs with pharmacogenomic evidence and related genetic testing in the National Institutes of Health All of Us database. Most patients with cancer (19 633 (88.3%) vs 2590 (11.7%)) received ≥1 drug and 36 (0.2%) received genetic testing, with a significant association between receiving ≥1 drug and age group (p<0.001), but not sex (p=0.612), race (p=0.232) or ethnicity (p=0.971). Drugs with pharmacogenomic evidence-but not genetic testing-were common for patients with cancer, reflecting key gaps preventing precision medicine from becoming standard of care.

Human-Comparable Sensitivity of Large Language Models in Identifying Eligible Studies Through Title and Abstract Screening: 3-Layer Strategy Using GPT-3.5 and GPT-4 for Systematic Reviews.

BACKGROUND: The screening process for systematic reviews is resource-intensive. Although previous machine learning solutions have reported reductions in workload, they risked excluding relevant papers. OBJECTIVE: We evaluated the performance of a 3-layer screening method using GPT-3.5 and GPT-4 to streamline the title and abstract-screening process for systematic reviews. Our goal is to develop a screening method that maximizes sensitivity for identifying relevant records. METHODS: We conducted screenings on 2 of our previous systematic reviews related to the treatment of bipolar disorder, with 1381 records from the first review and 3146 from the second. Screenings were conducted using GPT-3.5 (gpt-3.5-turbo-0125) and GPT-4 (gpt-4-0125-preview) across three layers: (1) research design, (2) target patients, and (3) interventions and controls. The 3-layer screening was conducted using prompts tailored to each study. During this process, information extraction according to each study's inclusion criteria and optimization for screening were carried out using a GPT-4-based flow without manual adjustments. Records were evaluated at each layer, and those meeting the inclusion criteria at all layers were subsequently judged as included. RESULTS: On each layer, both GPT-3.5 and GPT-4 were able to process about 110 records per minute, and the total time required for screening the first and second studies was approximately 1 hour and 2 hours, respectively. In the first study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.900/0.709 and 0.806/0.996, respectively. Both screenings by GPT-3.5 and GPT-4 judged all 6 records used for the meta-analysis as included. In the second study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.958/0.116 and 0.875/0.855, respectively. The sensitivities for the relevant records align with those of human evaluators: 0.867-1.000 for the first study and 0.776-0.979 for the second study. Both screenings by GPT-3.5 and GPT-4 judged all 9 records used for the meta-analysis as included. After accounting for justifiably excluded records by GPT-4, the sensitivities/specificities of the GPT-4 screening were 0.962/0.996 in the first study and 0.943/0.855 in the second study. Further investigation indicated that the cases incorrectly excluded by GPT-3.5 were due to a lack of domain knowledge, while the cases incorrectly excluded by GPT-4 were due to misinterpretations of the inclusion criteria. CONCLUSIONS: Our 3-layer screening method with GPT-4 demonstrated acceptable level of sensitivity and specificity that supports its practical application in systematic review screenings. Future research should aim to generalize this approach and explore its effectiveness in diverse settings, both medical and nonmedical, to fully establish its use and operational feasibility.

YouTube as a Source of Information on Public Health Ethics.

BACKGROUND: Public health ethics (PHE) is a dynamic area within bioethics that addresses the complex moral implications of public health measures in the face of growing health threats. YouTube is a powerful and widely used platform for disseminating health-related information. The primary objective of this study is to assess videos related to PHE on YouTube. The aim is to gauge the extent of misinformation in collecting PHE videos on the platform. METHODS: On October 25, 2023, a thorough investigation on YouTube was undertaken, employing pre-determined search phrases involving 'public health,' 'healthcare,' 'health services administration,' and 'health policy and ethics.' The research encompassed a total of 137 videos that were selected according to strict inclusion and exclusion criteria. The videos were evaluated using the Global Quality Scale to measure quality and the modified DISCERN tool to evaluate reliability. The researchers identified video sources and compared several video attributes across different quality groups. RESULTS: A total of 137 videos were analyzed, and 65 (47.45%) were classified as high quality, 52 (37.23%) as moderate quality, and 21 (15.32%) as low quality. In high-quality videos, academic, government, physician, and university-hospital sources predominated, whereas Internet users and news sources were connected with low-quality videos. Significant differences in DISCERN score, per day views, likes, and comments were seen across the quality groups (P = 0.001 for views per day and P = 0.001 for other characteristics). According to the findings, low-quality videos had higher median values for daily views, likes, and comments. CONCLUSION: Although nearly half of the videos were high-quality, low-quality videos attracted greater attention. Critical contributors to high-quality videos included academic, government, physician, and university-hospital sources. The findings highlight the importance of quality control methods on social media platforms and strategies to direct users to trustworthy health information. Authors should prioritize appropriate citations and evaluate YouTube and other comparable platforms for potential promotional low-quality information.

Competencies required to make use of Information Science and Technology among Japanese medical students: a cross-sectional study.

BACKGROUND: Competency in the use of information science and technology (IST) is essential for medical students. This study identified learning objectives and competencies that correspond with low self-assessment related to use of IST and factors that improve such self-assessment among medical students. METHODS: A questionnaire was administered to sixth-year medical students across 82 medical schools in Japan between November 2022 and February 2023. RESULTS: Three learning objectives were identified as difficult for the students to achieve: (1) provide an overview of the regulations, laws, and guidelines related to IST in medicine; (2) discuss ethical issues, such as social disparities caused by the digital divide that may arise in the use of IST in medicine; and (3) understand IST related to medical care. Further, problem-based learning, engaging with IST beyond class, and learning approach impacted the students' acquisition of competencies related to IST. Furthermore, it was recognized that the competencies required by medical students may change over the course of an updated medical school curriculum. CONCLUSIONS: It is important for medical students to recognize the significance of learning, establishing active learning methods, and gaining experience in practically applying these competencies.

Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities.

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications. Recommendations are provided with a means to engage in this mobilization effort.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

The Quality of Literature Search Reporting in Systematic Reviews Published in the Urological Literature (1998-2021).

PURPOSE: We evaluate to what extent systematic reviews published in the urological literature follow best practices for the reporting of searches. MATERIALS AND METHODS: Systematic reviews addressing questions of therapy/prevention were sought out in 5 major urological journals from January 1998 to December 2021. Two members performed study selection and data abstraction independently and in duplicate. The methodological and reporting quality of these systematic reviews was assessed using operationalized criteria based on the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-analyses-literature search extension) and PRISMA 2020 checklists. Proportions of systematic reviews that satisfied each criterion were compared based on period (1998-2012, 2013-2016, and 2017-2021) and journal of publication. RESULTS: The search identified 483 systematic reviews that met inclusion criteria. Most systematic reviews searched 2 or more electronic databases (88.6%); few searched abstract proceedings (26.7%), clinical trial registries (15.1%), or dedicated databases of the "gray literature" (6.2%). Approximately 1 in 3 systematic reviews (32.3%) were explicit in not restricting searches by language. A few criteria demonstrated improved reporting over time including use of clinical trial registries (6.8% vs 14.4% vs 23.3%; P = .001), searches unrestricted by language (37.3% vs 49.3% vs 55.1%; P = .006), and flow diagram reporting (34.8% vs 82.9% vs 93.2%; P = .001) but not the search of abstract proceedings (28.6% vs 24.0% vs 27.3%; P = .647). Reporting characteristics across journals were similar. CONCLUSIONS: Systematic reviews published in the urological literature have considerable shortcomings regarding the reporting of their underlying search strategies. Efforts must be taken to improve search strategies in the form of better training in systematic review methods as well as the more stringent enforcement of reporting guidelines.

Controversies in rheumatology: telemedicine-friend or foe?

Telemedicine is increasingly used in rheumatology. While telemedicine guaranteed care of patients during the COVID-19 pandemic, it is now increasingly used to facilitate triage of patients, monitoring of disease activity, and patients' education. In addition, tele-visits as well as remote physio- and psychotherapy are replacing traditional face-to-face contacts between patients and their healthcare provider. While this may save resources in a world in which the gap between the demand and the provision of healthcare increases, there is also a danger of losing essential information, for example by non-verbal communication, that can only be retrieved during face-to-face contact in the office. In addition, it may be challenging to build a trusting relationship between patients and healthcare professionals by virtual means only. Globally acting companies that see market opportunities already amply offer 'simple' technical solutions for telemedicine. While such solutions may seem (economically) interesting at first glance, there is a risk of monopolization, leaving the most valuable parts of healthcare to a small number of profit-seeking companies. In this article, the opportunities and threats of telemedicine in rheumatology are debated. A possible way forward is to complement traditional face-to-face visits with information gained by telemedicine, in order to render these consultations more efficient rather than replacing personal contact by technology.

Historical trends in reporting effect sizes in clinical neuropsychology journals: A call to venture beyond the results section.

OBJECTIVE: For decades, quantitative psychologists have recommended that authors report effect sizes to convey the magnitude and potential clinical relevance of statistical associations. However, fewer than one-third of neuropsychology articles published in the early 2000s reported effect sizes. This study re-examines the frequency and extent of effect size reporting in neuropsychology journal articles by manuscript section and over time. METHODS: A sample of 326 empirical articles were drawn from 36 randomly selected issues of six neuropsychology journals at 5-year intervals between 1995 and 2020. Four raters used a novel, reliable coding system to quantify the extent to which effect sizes were included in the major sections of all 326 articles. RESULTS: Findings showed medium-to-large increases in effect size reporting in the Methods and Results sections of neuropsychology journal articles that plateaued in recent years; however, there were only very small and nonsignificant changes in effect size reporting in the Abstract, Introduction, and Discussion sections. CONCLUSIONS: Authors in neuropsychology journals have markedly improved their effect size reporting in the core Methods and Results sections, but are still unlikely to consider these valuable metrics when motivating their study hypotheses and interpreting the conceptual and clinical implications of their findings. Recommendations are provided to encourage more widespread integration of effect sizes in neuropsychological research.

Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning.

BACKGROUND: The exponential growth of location-based social media (LBSM) data has ushered in novel prospects for investigating the urban food environment in health geography research. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. To enhance the analysis of the urban food environment using LBSM data, it is crucial to develop a more comprehensive list of food-related words. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities. METHODS: This study addresses the aforementioned research gap by introducing a novel methodology for expanding the food-related word dictionary and predicting energy densities. Seed words are generated from official and crowdsourced food composition databases, and new food words are discovered by clustering food words within the word embedding space using the Gaussian mixture model. Machine learning models are employed to predict the energy density classifications of these food words based on their feature vectors. To ensure a thorough exploration of the prediction problem, ten widely used machine learning models are evaluated. RESULTS: The approach successfully expands the food-related word dictionary and accurately predicts food energy density (reaching 91.62%.). Through a comparison of the newly expanded dictionary with the initial seed words and an analysis of Yelp reviews in the city of Toronto, we observe significant improvements in identifying food words and gaining a deeper understanding of the food environment. CONCLUSIONS: This study proposes a novel method to expand food-related vocabulary and predict the food energy density based on machine learning and word embedding. This method makes a valuable contribution to building a more comprehensive list of food words that can be used in geography and public health studies by mining geotagged social media data.

Development and evaluation of an online, patient-driven, family outreach intervention to facilitate sharing of genetic risk information in families with Lynch syndrome.

BACKGROUND: Identifying at-risk relatives of individuals with genetic conditions facilitates 'cascade' genetic testing and cancer prevention. Although current standards of care give mutation-positive (index) patients the responsibility of sharing genetic risk information with relatives, the communication is suboptimal, limited largely to close relatives. We developed FamilyCONNECT, a provider-mediated, patient-navigated online tool to facilitate family outreach, and assessed its feasibility, usability and acceptability. METHODS: (1) Development of the FamilyCONNECT prototype; (2) testing using online surveys of: (a) members of Lynch Syndrome (LS) International (LSI); (b) genetics service providers; and (3) hands-on testing with patients with LS. RESULTS: (1) FamilyCONNECT's features include introductory email to elicit participation, informational website/video, identity authentication/account creation, informed consent, sharing of genetic test results, pedigree expansion and process to invite at-risk relatives. (2a) 33% of the 170 LSI participants completed the survey. FamilyCONNECT's features received favourable responses from at least 79% of respondents. Unfavourable responses were for length of the consent document and mistrust of opening emailed links. (2b) Thirty-five genetics professionals responded to the providers' survey. Key perceived barriers to FamilyCONNECT's usage were privacy/confidentiality (83%), a lack of institutional resources (76%), a defined process (66%) and time (69%). (3) Ten patients navigated data collection fields and provided feedback for improvements. CONCLUSION: FamilyCONNECT tool's content and features were well received among patients with LS as well as providers. The tool could be a viable alternative to increase family outreach among patients with LS. Future efforts will focus on refining FamilyCONNECT and assessing its uptake and utilisation by patients with LS.

Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator.

Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.

Similar Outcomes of Web-Based and Face-to-Face Training of the GRADE Approach for the Certainty of Evidence: Randomized Controlled Trial.

BACKGROUND: The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach is a system for transparent evaluation of the certainty of evidence used in clinical practice guidelines and systematic reviews. GRADE is a key part of evidence-based medicine (EBM) training of health care professionals. OBJECTIVE: This study aimed to compare web-based and face-to-face methods of teaching the GRADE approach for evidence assessment. METHODS: A randomized controlled trial was conducted on 2 delivery modes of GRADE education integrated into a course on research methodology and EBM with third-year medical students. Education was based on the Cochrane Interactive Learning "Interpreting the findings" module, which had a duration of 90 minutes. The web-based group received the web-based asynchronous training, whereas the face-to-face group had an in-person seminar with a lecturer. The main outcome measure was the score on a 5-question test that assessed confidence interval interpretation and overall certainty of evidence, among others. Secondary outcomes included writing a recommendation for practice and course satisfaction. RESULTS: In all, 50 participants received the web-based intervention, and 47 participants received the face-to-face intervention. The groups did not differ in the overall scores for the Cochrane Interactive Learning test, with a median of 2 (95% CI 1.0-2.0) correct answers for the web-based group and 2 (95% CI 1.3-3.0) correct answers for the face-to-face group. Both groups gave the most correct answers to the question about rating a body of evidence (35/50, 70% and 24/47, 51% for the web-based and face-to-face group, respectively). The face-to-face group better answered the question about the overall certainty of evidence question. The understanding of the Summary of Findings table did not differ significantly between the groups, with a median of 3 correct answers to 4 questions for both groups (P=.352). The writing style for the recommendations for practice also did not differ between the 2 groups. Students' recommendations mostly reflected the strengths of the recommendations and focused on the target population, but they used passive words and rarely mentioned the setting for the recommendation. The language of the recommendations was mostly patient centered. Course satisfaction was high in both groups. CONCLUSIONS: Training in the GRADE approach could be equally effective when delivered asynchronously on the web or face-to-face. TRIAL REGISTRATION: Open Science Framework akpq7; https://osf.io/akpq7/.

Descriptive forest: experiments on a novel tree-structure-generalization method for describing cardiovascular diseases.

BACKGROUND: A decision tree is a crucial tool for describing the factors related to cardiovascular disease (CVD) risk and for predicting and explaining it for patients. Notably, the decision tree must be simplified because patients may have different primary topics or factors related to the CVD risk. Many decision trees can describe the data collected from multiple environmental heart disease risk datasets or a forest, where each tree describes the CVD risk for each primary topic. METHODS: We demonstrate the presence of trees, or a forest, using an integrated CVD dataset obtained from multiple datasets. Moreover, we apply a novel method to an association-rule tree to discover each primary topic hidden within a dataset. To generalize the tree structure for descriptive tasks, each primary topic is a boundary node acting as a root node of a C4.5 tree with the least prodigality for the tree structure (PTS). All trees are assigned to a descriptive forest describing the CVD risks in a dataset. A descriptive forest is used to describe each CVD patient's primary risk topics and related factors. We describe eight primary topics in a descriptive forest acquired from 918 records of a heart failure-prediction dataset with 11 features obtained from five datasets. We apply the proposed method to 253,680 records with 22 features from imbalanced classes of a heart disease health-indicators dataset. RESULTS: The usability of the descriptive forest is demonstrated by a comparative study (on qualitative and quantitative tasks of the CVD-risk explanation) with a C4.5 tree generated from the same dataset but with the least PTS. The qualitative descriptive task confirms that compared to a single C4.5 tree, the descriptive forest is more flexible and can better describe the CVD risk, whereas the quantitative descriptive task confirms that it achieved higher coverage (recall) and correctness (accuracy and precision) and provided more detailed explanations. Additionally, for these tasks, the descriptive forest still outperforms the C4.5 tree. To reduce the problem of imbalanced classes, the ratio of classes in each subdataset generating each tree is investigated. CONCLUSION: The results provide confidence for using the descriptive forest.

Application of text mining to the development and validation of a geographic search filter to facilitate evidence retrieval in Ovid MEDLINE: An example from the United States.

BACKGROUND: Given the increasing volume of published research in bibliographic databases, efficient retrieval of evidence is crucial and represents an opportunity to integrate novel techniques such as text mining. OBJECTIVES: To develop and validate a geographic search filter for identifying research from the United States (US) in Ovid MEDLINE. METHODS: US and non-US citations were collected from bibliographies of evidence-based reviews. Citations were partitioned by US/non-US status and randomly divided to a training and testing set. Using text mining, common one- and two-word terms in title/abstract fields were identified, and frequencies compared between US/non-US citations. RESULTS: Common US-related terms included (as ratio of frequency in US/non-US citations) US populations and geographic terms [e.g., 'Americans' (15.5), 'Baltimore' (20.0)]. Common non-US terms were non-US geographic terms [e.g., 'Japan' (0.04), 'French' (0.05)]. A search filter was developed with 98.3% sensitivity and 82.7% specificity. DISCUSSION: This search filter will streamline the identification of evidence from the US. Periodic updates may be necessary to reflect changes in MEDLINE's controlled vocabulary. CONCLUSION: Text mining was instrumental to the development of this search filter. A novel technique generated a gold standard set comprising >20,000 citations. This method may be adapted to develop subsequent geographic search filters.

Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak.

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013-2015.

BACKGROUND: Dengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China. METHODS: This study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013-2015 in mainland China at the provincial level. RESULTS: DF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August-November, influenza appeared during November-next March, and HFMD happened during April-November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases. CONCLUSIONS: This study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.

Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems.

BACKGROUND: One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset. MATERIALS AND METHODS: This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts' consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset. RESULTS: The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset. CONCLUSIONS: We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.