Medical Research Findings Research Articles

Recommendation for the use of respiratory syncytial virus vaccines.

Respiratory syncytial virus (RSV) is the most common pathogen for young children hospitalized with bronchiolitis and pneumonia. Most infections occur below 1 year of age. RSV is also a significant viral pathogen for adults with respiratory tract infection. Vaccines targeting the pre-fusion protein of RSV, including recombinant and mRNA vaccines, are now available. A committee of experts from related fields was convened by the Taiwan Immunization Vision and Strategy to develop recommendations for RSV vaccination in the elderly and pregnant women. The recommendation is not intended as a sole source of guidance in the prevention of RSV infection in children. The provisions listed in this recommendation are comprehensive suggestions made by experts in Taiwan based on existing medical evidence. This recommendation should be subject to modification in light of additional medical research findings in the future, and these provisions should not be cited as a basis for dispute resolution.

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.

Dissemination of medical research findings among medical researchers in a tertiary institution in Uganda

Background: Dissemination of research findings is a key obligation for researchers. It increases access to evidence and the ability to use and apply the evidence. Repackaging of research findings to inform policy and practice is not yet embraced in many low-and-middle income countries that have under-resourced health care systems. Objective: To determine the methods of communication of research findings by researchers at the Makerere University College of Health Sciences as well as the facilitators and barriers faced while disseminating the findings. Methods: This was a concurrent nested mixed-methods study among researchers. Key informant interviews and self-administered questionnaires were used. The collected Qualitative data was examined through thematic analysis. Quantitative data were analysed with STATA version 15.0, analysing categorical variables using frequencies and percentages. Results: Of 176 researchers involved in the quantitative survey, more than half (60%, n=106) were males and 40.9%(n=84/176) were lecturers. The most used dissemination method was journal publications (71.6%, n=126) followed by presentations in conferences/workshops (62.5%, n=110). Twelve researchers participated as key informant interviewees. Themes that emerged included: benefits, facilitators and challenges faced in research communication. Conclusions: Research findings are commonly disseminated through journals and conference presentations. However, researchers face challenges like financial constraints, time limitations, and misrepresentation of findings. Keywords: Dissemination of medical research; Journal Publication; Community engagement.

Do infographics ‘spin’ the findings of health and medical research?

ObjectiveTo compare the prevalence of ‘spin’, and specific reporting strategies for spin, between infographics, abstracts and full texts of randomised controlled trials (RCTs) reporting non-significant findings in the field of...

Interactional metadiscourse in translated and non-translated medical research article abstracts: a corpus-assisted study

ABSTRACT This study examines the linguistic and rhetorical characteristics of English medical research article abstracts (RAAs), a crucial medium for the global dissemination of medical information. Two corpora were compiled for analysis: the first consisting of non-translated English medical RAAs sourced from ten leading English-language medical journals, and the second of English medical RAAs translated from ten prominent Chinese medical journals. The findings of the study reveal several key points: 1) Translated medical RAAs exhibit lower levels of tentativeness, primarily due to a significant underrepresentation of hedges (e.g. likely); 2) Translated medical RAAs display higher levels of assertiveness, as indicated by the more frequent use of boosters (e.g. significantly); and 3) No significant difference was observed between the two types of medical RAAs in terms of their use of attitude markers, indicating a similarity in the attitudinal approach when presenting medical research findings in both translated and non-translated medical RAAs. These divergences can be attributed to the unique rhetorical and disciplinary conventions that govern the dissemination of medical knowledge in China compared to Western countries, highlighting the influence of cultural and linguistic norms on scientific communication. This investigation offers novel insights into the translation of medical RAAs, shedding light on cross-cultural divergences in the presentation of medical findings and enriching the discourse on scientific communication across languages.

Effect of Smoking on Cancer Surgery Outcomes and Recommendations for Perioperative Smoking Cessation Intervention

Smoking, a common behavior that causes health risks among adults around the world, is closely associated with the risks of developing various kinds of cancers. Nevertheless, the impact of smoking on preoperative risks and postoperative outcomes of cancer surgeries has been largely overlooked. Extensive basic medical and clinical research findings and prognosis data demonstrate that smoking is associated with increased risks of multiple perioperative complications, and that smoking affects the general prognosis of patients. Smoking cessation during the preoperative and perioperative periods effectively reduces these risks and improves the long-term postoperative outcomes of cancer patients. At present, health workers and patients are not giving adequate attention to smoking cessation interventions and the quality of interventions available is poor. Herein, we discussed the necessity of comprehensive and standardized smoking cessation services and made recommendations regarding the implementation of comprehensive and standardized smoking cessation services, providing support for cancer patients to access evidence-based care during the perioperative period.

Health Care Natural Language Processing (NLP) Using MOORA Method

The field of natural language processing (NLP) is being increasingly applied to the realm of healthcare. This abstract delves into the intersection of health care and NLP, highlighting how this technology is being used to enhance various aspects of the healthcare industry. From analyzing medical records and extracting relevant information to aiding in diagnosis and treatment recommendations, NLP is playing a pivotal role in transforming how healthcare professionals interact with data and make informed decisions. This abstract provides an overview of the key applications and benefits of employing NLP in healthcare, showcasing its potential to improve patient outcomes and streamline medical processes. This research seeks to harness the power of advanced computational linguistics to revolutionize various aspects of the healthcare industry. By enabling computers to understand and interpret human language, NLP has the potential to drive transformative changes NLP-powered systems can analyze vast amounts of medical literature, patient records, and research findings to provide healthcare practitioners with timely and relevant information. This assists in making well-informed decisions regarding diagnoses, treatment plans, and prognoses. NLP techniques facilitate the extraction of pertinent information from unstructured medical records, including patient histories and doctor's notes. This structured data can then be used for research, trend analysis, and epidemiological studies, contributing to evidence-based medicine. Versatile with unique alternatives a new method for optimization is proposed MOORA. This method is objective denotes the matrix of responses of the alternatives, however, proposing better policies, which rates are used. Well established. Multi-objective another method for optimization is used for comparison, reference point method. Then, various competitions this proved to be the best choice among the methods. From the result BC2GM is in 1st rank whereas BC5- Disease is in lowest rank.

Health Care Natural Language Processing (NLP) Using MOORA Method

The field of natural language processing (NLP) is being increasingly applied to the realm of healthcare. This abstract delves into the intersection of health care and NLP, highlighting how this technology is being used to enhance various aspects of the healthcare industry. From analyzing medical records and extracting relevant information to aiding in diagnosis and treatment recommendations, NLP is playing a pivotal role in transforming how healthcare professionals interact with data and make informed decisions. This abstract provides an overview of the key applications and benefits of employing NLP in healthcare, showcasing its potential to improve patient outcomes and streamline medical processes. This research seeks to harness the power of advanced computational linguistics to revolutionize various aspects of the healthcare industry. By enabling computers to understand and interpret human language, NLP has the potential to drive transformative changes NLP-powered systems can analyze vast amounts of medical literature, patient records, and research findings to provide healthcare practitioners with timely and relevant information. This assists in making well-informed decisions regarding diagnoses, treatment plans, and prognoses. NLP techniques facilitate the extraction of pertinent information from unstructured medical records, including patient histories and doctor's notes. This structured data can then be used for research, trend analysis, and epidemiological studies, contributing to evidence-based medicine. Versatile with unique alternatives a new method for optimization is proposed MOORA. This method is objective denotes the matrix of responses of the alternatives, however, proposing better policies, which rates are used. Well established. Multi-objective another method for optimization is used for comparison, reference point method. Then, various competitions this proved to be the best choice among the methods. From the result BC2GM is in 1st rank whereas BC5- Disease is in lowest rank.

O.19.6 - How are new medical research findings in urology communicated to patients and the public?: Presenter(s): Cait Bleakley, Royal Devon University Hospitals, United Kingdom

O.19.6 - How are new medical research findings in urology communicated to patients and the public?: Presenter(s): Cait Bleakley, Royal Devon University Hospitals, United Kingdom

Biostatistics for gastroenterologists. Part II ̶ Rethinking sample size.

Sample size determination is a critical aspect of medical studies, influencing the reliability and generalizability of research findings. This article explores the importance of sample size in both basic and clinical research. The considerations for sample size differ based on the type of research, whether it is involving humans, animals, or cells. In basic research, a larger sample size is necessary to ensure statistical power and reliable results, enhancing the precision and generalizability of findings. In clinical research, determining an appropriate sample size is crucial for obtaining valid and clinically relevant results, ensuring sufficient statistical power to detect differences between treatment groups or validate intervention efficacy. Reporting sample size calculations accurately and complying with reporting guidelines, such as the CONSORT Statement, is essential for transparent and comprehensive research publications. Consulting a statistician is highly recommended to ensure appropriate sample size determination, enhance scientific rigor, and obtain reliable and clinically relevant findings in medical research.

Causes of the male-female ratio of depression based on the psychosocial factors.

Recently, depression has been a great concern of researchers, and the research on the male-female ratio of depression mainly covers the fields of epidemiology and clinical studies. This study is made to explore the causes of the male-female ratio of depression from a humanistic perspective, namely, the psychosocial factors, thus providing some support and reference for further research on depression. Thereby, the authors select and analyze word lists closely collocated with "depression" according to word score and frequency through the word sketch in enTenTen20 by the corpus tool "Sketch Engine." Meanwhile, the above words are further explored for the causes of the male-female ratio in depression from the biological, psychological, and social aspects based on the Biopsychosocial Model as well as some existing relevant medical research findings. Consequently, women are found to be more likely to suffer from emotional problems and depression than men under the influence of biological basis, general psychological characteristics, and social environment interference.

How Do Orthopedic Surgeons Stay Up-to-Date With the Literature? A Survey.

Background: With the rise of social media and other web-based platforms, there has been a shift in how medical research findings are disseminated. Questions/Purposes: We sought to investigate how often orthopedic surgeons use different knowledge-seeking methods and how confident they were in their ability to stay up-to-date. Methods: We distributed a 23-question survey to 120 orthopedic surgeons employed at a single tertiary-care metropolitan orthopedic hospital. The survey collected the surgeons' use of reference managers, RSS feeds, and preferred methods of staying up-to-date. Respondents were asked to report the number of hours they spent staying up-to-date per week and their success with keeping up with orthopedic specialty literature. Results: A total of 85 physicians responded (median age, 52 years); 83% of respondents who spent 3 or more hours a week in keeping up-to-date with the literature said they felt successful in doing so, while 40% of participants who spent less than 1 hour a week said they felt successful. Fifty-five percent of surgeons under the median age used social media, while only 29% of surgeons over the median age used social media. Conclusions: This survey suggests that spending only 1 to 2 hours per week was enough to make the orthopedic surgeons we surveyed feel confident that they were keeping up with the literature in their specialty. These findings also suggest that it may be more important for surgeons to choose a convenient method of literature review that will enable them to maximize time spent reading, rather than attempting to use any one source.

Evaluating the Accuracy and Design of Visual Abstracts in Academic Surgical Journals.

The objective of this study was to assess the quality and accuracy of visual abstracts published in academic surgical journals. Visual abstracts are commonly used to disseminate medical research findings. They distill the key messages of a research article, presenting them graphically in an engaging manner so that potential readers can decide whether to read the complete manuscript. We developed the Visual Abstract Assessment Tool based upon published guidelines. Seven reviewers underwent iterative training to apply the tool. We collected visual abstracts published by 25 surgical journals from January 2017 to April 2021; those corresponding to systematic reviews without meta-analysis, conference abstracts, narrative reviews, video abstracts, or nonclinical research were excluded. Included visual abstracts were scored on accuracy (as compared with written abstracts) and design, and were given a "first impression" score. Across 25 surgical journals 1325 visual abstracts were scored. We found accuracy deficits in the reporting of study design (35.8%), appropriate icon use (49%), and sample size reporting (69.2%), and design deficits in element alignment (54.8%) and symmetry (36.1%). Overall scores ranged from 9 to 14 (out of 15), accuracy scores from 4 to 8 (out of 8), and design scores from 3 to 7 (out of 7). No predictors of visual abstract score were identified. Visual abstracts vary widely in quality. As visual abstracts become integrated with the traditional components of scientific publication, they must be held to similarly high standards. We propose a checklist to be used by authors and journals to standardize the quality of visual abstracts.

To tweet or not to tweet in otorhinolaryngology, that is the question

ObjectiveTo describe the scientific medical literature devoted to the various interactions between otorhinolaryngology (ORL) and social networks. Material and methodsA literature search, conducted on December 15, 2021, using the search engine of the United States National Library of Medicine (PubMed) and the key-words “otorhinolaryngology”, “social network”, and “Twitter”, retrieved 321 articles for which Abstracts were read for selection of articles with qualitative and quantitative data regarding the various relationships between ORL and social media. ResultsForty-four articles were selected and analyzed. Thirty-nine originated from the USA and none from France or French-speaking countries. Only 1 article was a prospective randomized study. Schematically, two main types of publication were identified. The first dealt with user interactions, topics discussed, teaching of the ORL specialty and publicizing research. The second dealt with the limits and dangers of this new means of publicizing scientific thinking in our specialty. Reading these articles highlighted the role of social media in publicizing ORL research, and suggested distinct options to improve interactions between otorhinolaryngologists, patients and society as a whole. ConclusionAt a time when society is demanding rapid access to medical research findings, just as it is mandatory to master and adhere to the rules for medical writing in research, it is also now necessary to learn how to communicate via social media and send a tweet if one wishes to publicize its research and/or exchange with patients.

Data Science Methods to Develop Decision Support Systems for Real-time Monitoring of COVID-19 Outbreak

Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports has been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner, which includes time trends, epidemic curves, and key epidemiological parameters such as growth rate, doubling time, and effective reproduction number, has been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision-aiding tools. Doi: 10.28991/HEF-2022-03-02-08 Full Text: PDF

Integrating Medical Monitoring and Research Findings to Optimize Care for Those Responding to the September 11 (2001) Terrorist Attacks in the U.S.

Integrating Medical Monitoring and Research Findings to Optimize Care for Those Responding to the September 11 (2001) Terrorist Attacks in the U.S.

Technology Acceptance Models to Improve Adoption of Health Information Systems

Technology acceptance model (TAM) used to enhance understanding of technology acceptance in the workplace as healthcare environment. The use of technology in the healthcare environment improves healthcare outcomes, control the healthcare data, improve collaboration and skills among healthcare staff and reduce organizational expenses. Recent literature studies done indicated lack of adoption of technology as the health information systems (HISs), this be affect on collaboration among healthcare staff this in turn affect on healthcare outcomes and medical research findings. This research attempts to address the aforementioned issues by applying the extended TAM based on relevant models. This research identifies success factors influencing the adoption of HISs in healthcare environment. This research employed the exploratory qualitative research method to collections the required data. Results of this study shows that factors such as data confidentiality "security and privacy" becoming more important in addition to current factors such as perceived usefulness, ease of use, quality” system and information". Beside to, privacy factor have significantly affect the intent to use and the adoption of HISs by healthcare staff. © 2018 JASET, International Scholars and Researchers Association

On the Multidisciplinary Perspective of Medical Education Model Research and Its Impact on Medical Students' Smoking Behavior

Medical education research is relatively an emerging field, which is not only because of its interdisciplinary nature of pedagogy and medicine, but more importantly, the methods and findings of medical education research are borrowed from research results and literature in the fields of pedagogy, history, sociology, cognition and psychology. This determines the multidisciplinary nature of medical education research. In fact, many research problems are complex and diverse, which requires the use of knowledge and resources in multiple disciplines and fields for understanding or solution. Hence, now more emphasis is placed on interdisciplinary research, rather than pure multidisciplinary research, which is to integrate the theories, thinking and methods of multiple disciplines to jointly study a problem. In medical education research, appropriate use of theories and methods of different disciplines may produce new theories and methods, which in turn may be used for reference by other disciplines and fields. On the premise of this multi-disciplinary perspective, this paper discusses the impact of the change of medical education model on the smoking behavior of medical students, and points out that the non-smoking behavior of medical students is especially affected by the interaction between the role model behavior of teachers in medical colleges and the teaching humanistic environment, Finally, the conclusion is that the research on medical education similar to complex cases needs to be examined from the perspective of integrated thinking and multi-disciplinary.

Data interpretation and visualization of COVID-19 cases using R programming

BackgroundData analysis and visualization are essential for exploring and communicating medical research findings, especially when working with COVID records. ResultsData on COVID-19 diagnosed cases and deaths from December 2019 is collected automatically from www.statista.com, datahub.io, and the Multidisciplinary Digital Publishing Institute (MDPI). We have developed an application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using Statista, Data Hub, and MDPI data from densely populated countries like the United States, Japan, and India using R programming. ConclusionsThe COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application will help with a better understanding of the SARS-CoV-2 epidemic worldwide.

COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic

BackgroundData analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance.ResultsData on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number.ConclusionsThe COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.