High Yield Medical Reviews最新文献

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection 从数据到诊断：用于乳腺癌检测的开放式乳腺 X 射线照相数据库的叙述性回顾

High Yield Medical Reviews Pub Date : 2024-06-01 DOI: 10.59707/hymrpfnz8344

Jaber H Jaradat, Raghad Amro, R. Hamamreh, Ayman Musleh, Mahmoud Abdelgalil

引用次数: 0

Analysis of retracted articles by Jordanian authors 对约旦作者被撤稿文章的分析

High Yield Medical Reviews Pub Date : 2023-12-09 DOI: 10.59707/hymrwesp3396

Naseem Zghoul, Rana Sultan AlKhraisha, Yara Odeh Odeh, Aya Rashid Rashid, Ruby Jamali, S. A. Alryalat

{"title":"Analysis of retracted articles by Jordanian authors","authors":"Naseem Zghoul, Rana Sultan AlKhraisha, Yara Odeh Odeh, Aya Rashid Rashid, Ruby Jamali, S. A. Alryalat","doi":"10.59707/hymrwesp3396","DOIUrl":"https://doi.org/10.59707/hymrwesp3396","url":null,"abstract":"Objective: The aim of this study is to analyze retracted articlespublished by Jordanian authors in the period between 2001 to2022. \u0000 \u0000Method: This paper was done by using data from the Retraction Watch database filtered to include papers where one of the authors was affiliated to Jordan covering the period between2001 to 2022. \u0000 \u0000Results: The search yielded a total of 40 articles retracted with authors affiliated to Jordan for papers published from 2001 to 2022, as reported in the retraction watch database. The number of retractions in the last 5 years has been increasing. Regardingfields, medicine was the most common with 50% of retractions, followed by technology and engineering. The total number of authors in this research was 132, out of them 79 authors were from Jordan. Five authors had two retractions, while the rest had one retraction. Of the total retractions, 7(18%) were from the University of jordan. Followed by Jordan University of Science and Technology (JUST) with 6 (15%.) retractions. In regard to the reason for retraction, author and data related disputes were the most common. \u0000 \u0000Conclusion: retractions in articles published by Jordanian authors has been increasing throughout the last few years, with highest researching universities having highest number of retractions. Awareness about data and author related reasons for retractions may lower retractions in Jordan.","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"25 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing Large Language Models in Medical Research and Scientific Writing: A Closer Look to The Future 在医学研究和科学写作中利用大型语言模型：展望未来

High Yield Medical Reviews Pub Date : 2023-12-09 DOI: 10.59707/hymrfbya5348

Mohammad Abu-Jeyyab, Sallam Alrosan, I. Alkhawaldeh

引用次数: 0

ChatGPT Role in a Medical Survey ChatGPT 在医学调查中的作用

High Yield Medical Reviews Pub Date : 2023-12-09 DOI: 10.59707/hymrtffp5435

Abdel Rahman Feras AlSamhori, J. AlSamhori, Ahmad Feras AlSamhori

{"title":"ChatGPT Role in a Medical Survey","authors":"Abdel Rahman Feras AlSamhori, J. AlSamhori, Ahmad Feras AlSamhori","doi":"10.59707/hymrtffp5435","DOIUrl":"https://doi.org/10.59707/hymrtffp5435","url":null,"abstract":"Significant progress has been made in AI over the past decade, but its application in clinical care remains limited. However, ChatGPT, an advanced language model developed by OpenAI, shows great promise in medicine and can significantly impact medical surveys by improving data collection and generating valuable insights for better healthcare outcomes. ChatGPT has the potential to enhance survey research by assisting in various aspects, including survey design, sampling, data cleaning, analysis, and reporting, improving the quality and efficiency of the research process. AI chatbots like ChatGPT in survey administration can enhance response rates and participant engagement, providing a better user experience and capturing more comprehensive data. Numerous studies have demonstrated ChatGPT's impressive performance in clinical reasoning exams, addressing complex questions in pathology, microbiology, and life support scenarios, making it a valuable tool for data analysis and decision-making in healthcare. While using ChatGPT in medical surveys offers advantages such as accessibility, language versatility, knowledge democratization, and efficiency, there are also disadvantages, including response sensitivity, data limitations, accuracy concerns, bias, and limited access to recent literature. Ethical concerns in AI healthcare include privacy issues, mistrust in AI systems, societal prejudices, and racial biases, which can be addressed through privacy protection measures, transparency, trust-building efforts, bias mitigation strategies, and involving relevant stakeholders in the process. \u0000 ","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reporting studies conducted using Open Access Data (ROAD) guideline statement 报告使用开放式获取数据（ROAD）准则声明进行的研究

High Yield Medical Reviews Pub Date : 2023-12-09 DOI: 10.59707/hymrunie2175

S. A. Alryalat, Lna Malkawi, Randa I. Farah

{"title":"Reporting studies conducted using Open Access Data (ROAD) guideline statement","authors":"S. A. Alryalat, Lna Malkawi, Randa I. Farah","doi":"10.59707/hymrunie2175","DOIUrl":"https://doi.org/10.59707/hymrunie2175","url":null,"abstract":"The growing availability of open access data presents numerous opportunities for researchers, but also raises challenges in terms of adequately reporting methods and findings. This article presents the Reporting of Studies Conducted using Open Access Data (ROAD) guidelines: a comprehensive, practical framework developed to standardize and improve the reporting of research using open access data. The guidelines were built upon existing principles for observational studies, tailored specifically to address the context of open data use. Their development involved an extensive review of published open data studies, and input from a diverse panel of experts through a series of consensus meetings. The ROAD guidelines encompass various aspects of study reporting, including specifying the original dataset, articulating study design and setting, detailing participant selection and variables, and acknowledging data providers. By enhancing transparency and reproducibility, these guidelines aim to improve the quality of research reports, ensure accurate interpretation of results , and foster more effective use of open access data in the scientific community. We invite feedback and further refinement from researchers and practitioners to ensure the continued relevance of the ROAD guidelines in the dynamic landscape of open data research.","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"8 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Mining of Systematic Reviews 1934-2023: A Bibliometric Analysis 系统评论1934-2023的数据挖掘:文献计量学分析

High Yield Medical Reviews Pub Date : 2023-06-01 DOI: 10.59707/hymrhuhp8885

Haneen Al-Abdallat, Badi Rawashdeh

{"title":"Data Mining of Systematic Reviews 1934-2023: A Bibliometric Analysis","authors":"Haneen Al-Abdallat, Badi Rawashdeh","doi":"10.59707/hymrhuhp8885","DOIUrl":"https://doi.org/10.59707/hymrhuhp8885","url":null,"abstract":"Introduction \u0000Systematic reviews consolidate evidence and drive clinical practice guidelines, cost-effective analyses, and policy decisions; therefore, their annual publication rate has increased significantly. We used bibliometric analysis to identify research trends, the most searched topics, authors and organizations productivity and collaboration, the research network, and research gaps by examining keywords frequency and systematic reviews distribution.\u0000Methods \u0000We searched the PubMed database for systematic reviews using the systematic review filter described by Salvador-Oliván and coauthors, which has higher recall than the PubMed SR filter. The search period was from 1934 until February 3, 2023. Microsoft Excel and the VOSviewer application were used for analyzing yearly trends, institutions, authors, and keywords, as well as to create tables and network figures.\u0000Results \u0000A total of 378,685 articles were published. The number of articles published has been rising steadily during the past five years. The University of Toronto and McMaster University in Canada (n = 1415 and n = 1386) were the leading contributory universities. “Genetic predisposition to disease”, “postoperative complications”, “neoplasm”, “stroke”, and “covid-19” were the top 5 occurring keywords that are particular to a specialty in systematic reviews.\u0000Conclusion \u0000This bibliometric research examined systematic reviews, publication trends, the majority of publishing disciplines, authors and organizations productivity, and collaborative efforts. The results of this study could prove to be an invaluable resource for researchers, policymakers, and healthcare professionals.","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"30 13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133150526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of Topics Published in Leading Medical Journals Using Natural Language Processing 使用自然语言处理评估发表在主要医学期刊上的主题

High Yield Medical Reviews Pub Date : 2023-06-01 DOI: 10.59707/hymrhmdo2739

S. A. Alryalat, Ahmad Qasem, Karam Albdour, Badi Rawashdeh

{"title":"Assessment of Topics Published in Leading Medical Journals Using Natural Language Processing","authors":"S. A. Alryalat, Ahmad Qasem, Karam Albdour, Badi Rawashdeh","doi":"10.59707/hymrhmdo2739","DOIUrl":"https://doi.org/10.59707/hymrhmdo2739","url":null,"abstract":"Introduction: Topic detection can be used to identify trends in literature, providing valuable insight into the direction of the field. We developed a natural language processing (NLP) based method to identify topics from given abstracts and assessed the main topics of published articles by top medical journals in the last three years. \u0000Methods: This study utilized a two-part methodology to extract and classify original articles published by four non-specialized medical journals; Lancet, New England Journal of Medicine, Journal of the American Medical Association, and British Medical Journal. The first part employed bibliometric data collection to search for original articles published between 2020 and 2022. The second part used an NLP approach based on the BERTopic model to classify the articles included into separate topics. \u0000Results: The model was able to classify 1,540 articles out of the included 2,081 (79.42%) into 39 different topics in 11 fields. COVID-19-related and cancer treatment-related articles constituted approximately 25% and 7% of all published papers during 2020-2022 respectively. The study found that each of the included general medical journal tended to focus on certain topics more than others. \u0000Conclusion: We identified a new methodology that can identify topics discussed in medical literature from abstracts as an input. We also demonstrated the potential of this methodology for analyzing trends in medical literature more efficiently and effectively. This study's methodology can be replicated on a larger scale with more papers, more journals, and over a longer period, highlighting the importance of further research using NLP models.","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"100 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods of Converting Effect Measures Used in Meta-analysis: A Narrative Review and A Practical Guide. meta分析中转换效应测量的方法:叙述回顾与实践指南。

High Yield Medical Reviews Pub Date : 2023-04-03 DOI: 10.59707/hymrrjkr4024

Ahmad A. Toubasi, Thuraya Al-Sayegh, Rama Rayyan

引用次数: 0