57 datasets found
  1. Data (i.e., evidence) about evidence based medicine

    • figshare.com
    • search.datacite.org
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    Updated May 30, 2023
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    Jorge H Ramirez (2023). Data (i.e., evidence) about evidence based medicine [Dataset]. http://doi.org/10.6084/m9.figshare.1093997.v24
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    pngAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Jorge H Ramirez
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

    1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
    2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
    3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
      Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
    4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
    5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
    6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
    7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
    8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
    9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
    10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
    11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
    12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
    13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
    14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
    15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---
  2. r

    The Journal of Community Health Management Impact Factor 2024-2025 -...

    • researchhelpdesk.org
    Updated Feb 23, 2022
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    Research Help Desk (2022). The Journal of Community Health Management Impact Factor 2024-2025 - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/impact-factor-if/523/the-journal-of-community-health-management
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    Dataset updated
    Feb 23, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    The Journal of Community Health Management Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Community Health Management (JCHM) is open access, double-blind peer-review journal publishing quarterly since 2014. JCHM is proclaimed by Innovative Education and Scientific Research Foundation, print and published by Innovative Publication. It has an International Standard Serial Number (ISSN 2394-272X, e ISSN 2394-2738). JCHM permits authors to self-archive final approval of the articles on any OAI-compliant institutional/subject-based repository. Aim and Scope JCHM is focusing on Community Health which is the branch of the Public Health, it's making people aware and describing their role as determinants of their own and other people’s health in contrast to environmental health which focal point on the physical environment and its impact on people health. It concentrates on the maintenance, protection, and improvement of the health status of population groups and communities. The scope is, therefore, huge covering almost all streams of Community Health Management starting from original research articles, review articles, short communications, and clinical cases as well as studies covering clinical, experimental and applied topics on Community health Management on above subjective areas. The scope of the journal isn't restricted to those subjects however it's the broader coverage of all the newest updates and specialties. Indexing The Journal is an index with Index Copernicus (Poland), Google Scholar, J-gate, EBSCO (USA) database, Academia.edu, CrossRef, ROAD, InfoBase Index, GENAMIC, etc. Keywords Acute Care, Bio-statics, Community Health, Epidemiology and Health Services Research, Health Management, Medicine and Allied branches of Medical Sciences including Health Statistics, Nutrition, Preventive Medicine, Primary Prevention, Primary Health Care, Secondary Prevention, Secondary Healthcare, Tertiary Healthcare.

  3. Z

    Data from: Datasets for publication: 'Measuring the excellence contribution...

    • data.niaid.nih.gov
    • produccioncientifica.ugr.es
    • +1more
    Updated Nov 12, 2021
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    Glänzel, Wolfgang (2021). Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor' [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5676183
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    Dataset updated
    Nov 12, 2021
    Dataset provided by
    Torres-Salinas, Daniel
    Arroyo-Machado, Wenceslao
    Glänzel, Wolfgang
    Ulrych, Ursula
    Gorraiz, Juan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor'.

    Overview. Overview of the number of journals, publications, excellent publications and multidisciplinarity for each category considered.

    ALL. Journal indicators for all the document types by JCR category.

    ALL_JCR. Journal indicators for all the document types by JCR category (only journals indexed in the JCR category are taken into account).

    AR. Journal indicators for only articles and reviews by JCR category.

    AR_JCR. Journal indicators for only articles and reviews by JCR category (only journals indexed in the JCR category are taken into account).

  4. f

    Data from: Journals evaluation policy in the medical fields: impacts on...

    • figshare.com
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    Updated Jun 1, 2023
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    Luiz Roberto Curtinaz Schifini; Rosângela Schwarz Rodrigues (2023). Journals evaluation policy in the medical fields: impacts on brazilian editorial production [Dataset]. http://doi.org/10.6084/m9.figshare.11839803.v1
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    jpegAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    Luiz Roberto Curtinaz Schifini; Rosângela Schwarz Rodrigues
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ABSTRACT This research analyzes the characteristics of Qualis A1 medical journals in order to set up a critical reflection on the impact of journal evaluation policies on Brazilian scientific production. Identifies editorial characteristics of the A1 journals in the areas of Medicina I, Medicina II and Medicina III through a qualitative-quantitative analysis, in order to draw a profile of these journals. The information was extracted from the following systems: Sucupira, Ulrichsweb, DOAJ, Scimago Journal Rank and Journal Citation Reports. The results for the profile of the journals were homogeneous among the three medical areas, and demonstrated that they are mainly published by commercial entities with the predominance of the publisher Elsevier; the median of the unified factor (Journal Impact Factor or Cites per Doc) is 5,365; the frequency of publishing is monthly; they are 45 years old; 13% are Open Access; the predominant country is the United States and that the English language is almost unanimous. It concludes that the observed editorial characteristics reflect the hegemony of commercial conglomerates in the academic publishing market, and that the Brazilian journals, mostly of Open Access and financed by public resources, are unable to compete with the journals of these companies.

  5. n

    Data from: Sharing detailed research data is associated with increased...

    • data.niaid.nih.gov
    • dataone.org
    • +1more
    zip
    Updated May 26, 2011
    + more versions
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    Heather A. Piwowar; Roger S. Day; Douglas B. Fridsma (2011). Sharing detailed research data is associated with increased citation rate [Dataset]. http://doi.org/10.5061/dryad.j2c4g
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    zipAvailable download formats
    Dataset updated
    May 26, 2011
    Dataset provided by
    University of Pittsburgh School of Medicine
    Authors
    Heather A. Piwowar; Roger S. Day; Douglas B. Fridsma
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

  6. n

    Data of top 50 most cited articles about COVID-19 and the complications of...

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Jan 10, 2024
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    Tanya Singh; Jagadish Rao Padubidri; Pavanchand Shetty H; Matthew Antony Manoj; Therese Mary; Bhanu Thejaswi Pallempati (2024). Data of top 50 most cited articles about COVID-19 and the complications of COVID-19 [Dataset]. http://doi.org/10.5061/dryad.tx95x6b4m
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    zipAvailable download formats
    Dataset updated
    Jan 10, 2024
    Dataset provided by
    Kasturba Medical College, Mangalore
    Authors
    Tanya Singh; Jagadish Rao Padubidri; Pavanchand Shetty H; Matthew Antony Manoj; Therese Mary; Bhanu Thejaswi Pallempati
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Background This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems. Methods Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact factor, authors, study details, and patient demographics. Results The focus is primarily on 2020 publications (96%), with all articles being open-access. Leading journals include The Lancet, NEJM, and JAMA, with prominent contributions from Internal Medicine (46.9%) and Pulmonary Medicine (14.5%). China played a major role (34.9%), followed by France and Belgium. Clinical features were the primary study topic (68%), often utilizing retrospective designs (24%). Among 22,477 patients analyzed, 54.8% were male, with the most common age group being 26–65 years (63.2%). Complications affected 13.9% of patients, with a recovery rate of 57.8%. Conclusion Analyzing these top-cited articles offers clinicians and researchers a comprehensive, timely understanding of influential COVID-19 literature. This approach uncovers attributes contributing to high citations and provides authors with valuable insights for crafting impactful research. As a strategic tool, this analysis facilitates staying updated and making meaningful contributions to the dynamic field of COVID-19 research. Methods A bibliometric analysis of the most cited articles about COVID-19 complications was conducted in July 2021 using all journals indexed in Elsevier’s Scopus and Thomas Reuter’s Web of Science from November 1, 2019 to July 1, 2021. All journals were selected for inclusion regardless of country of origin, language, medical speciality, or electronic availability of articles or abstracts. The terms were combined as follows: (“COVID-19” OR “COVID19” OR “SARS-COV-2” OR “SARSCOV2” OR “SARS 2” OR “Novel coronavirus” OR “2019-nCov” OR “Coronavirus”) AND (“Complication” OR “Long Term Complication” OR “Post-Intensive Care Syndrome” OR “Venous Thromboembolism” OR “Acute Kidney Injury” OR “Acute Liver Injury” OR “Post COVID-19 Syndrome” OR “Acute Cardiac Injury” OR “Cardiac Arrest” OR “Stroke” OR “Embolism” OR “Septic Shock” OR “Disseminated Intravascular Coagulation” OR “Secondary Infection” OR “Blood Clots” OR “Cytokine Release Syndrome” OR “Paediatric Inflammatory Multisystem Syndrome” OR “Vaccine Induced Thrombosis with Thrombocytopenia Syndrome” OR “Aspergillosis” OR “Mucormycosis” OR “Autoimmune Thrombocytopenia Anaemia” OR “Immune Thrombocytopenia” OR “Subacute Thyroiditis” OR “Acute Respiratory Failure” OR “Acute Respiratory Distress Syndrome” OR “Pneumonia” OR “Subcutaneous Emphysema” OR “Pneumothorax” OR “Pneumomediastinum” OR “Encephalopathy” OR “Pancreatitis” OR “Chronic Fatigue” OR “Rhabdomyolysis” OR “Neurologic Complication” OR “Cardiovascular Complications” OR “Psychiatric Complication” OR “Respiratory Complication” OR “Cardiac Complication” OR “Vascular Complication” OR “Renal Complication” OR “Gastrointestinal Complication” OR “Haematological Complication” OR “Hepatobiliary Complication” OR “Musculoskeletal Complication” OR “Genitourinary Complication” OR “Otorhinolaryngology Complication” OR “Dermatological Complication” OR “Paediatric Complication” OR “Geriatric Complication” OR “Pregnancy Complication”) in the Title, Abstract or Keyword. A total of 5940 articles were accessed, of which the top 50 most cited articles about COVID-19 and Complications of COVID-19 were selected through Scopus. Each article was reviewed for its appropriateness for inclusion. The articles were independently reviewed by three researchers (JRP, MAM and TS) (Table 1). Differences in opinion with regard to article inclusion were resolved by consensus. The inclusion criteria specified articles that were focused on COVID-19 and Complications of COVID-19. Articles were excluded if they did not relate to COVID-19 and or complications of COVID-19, Basic Science Research and studies using animal models or phantoms. Review articles, Viewpoints, Guidelines, Perspectives and Meta-analysis were also excluded from the top 50 most-cited articles (Table 1). The top 50 most-cited articles were compiled in a single database and the relevant data was extracted. The database included: Article Title, Scopus Citations, Year of Publication, Journal, Journal Impact Factor, Authors, Number of Authors, Department Affiliation, Number of Institutions, Country of Origin, Study Topic, Study Design, Sample Size, Open Access, Non-Original Articles, Patient/Participants Age, Gender, Symptoms, Signs, Co-morbidities, Complications, Imaging Modalities Used and outcome.

  7. PubMed Datasets

    • brightdata.com
    .json, .csv, .xlsx
    Updated Jul 15, 2016
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    Bright Data (2016). PubMed Datasets [Dataset]. https://brightdata.com/products/datasets/pubmed
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    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Jul 15, 2016
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    Unlock valuable biomedical knowledge with our comprehensive PubMed Dataset, designed for researchers, analysts, and healthcare professionals to track medical advancements, explore drug discoveries, and analyze scientific literature.

    Dataset Features

    Scientific Articles & Abstracts: Access structured data from PubMed, including article titles, abstracts, authors, publication dates, and journal sources. Medical Research & Clinical Studies: Retrieve data on clinical trials, drug research, disease studies, and healthcare innovations. Keywords & MeSH Terms: Extract key medical subject headings (MeSH) and keywords to categorize and analyze research topics. Publication & Citation Data: Track citation counts, journal impact factors, and author affiliations for academic and industry research.

    Customizable Subsets for Specific Needs Our PubMed Dataset is fully customizable, allowing you to filter data based on publication date, research category, keywords, or specific journals. Whether you need broad coverage for medical research or focused data for pharmaceutical analysis, we tailor the dataset to your needs.

    Popular Use Cases

    Pharmaceutical Research & Drug Development: Analyze clinical trial data, drug efficacy studies, and emerging treatments. Medical & Healthcare Intelligence: Track disease outbreaks, healthcare trends, and advancements in medical technology. AI & Machine Learning Applications: Use structured biomedical data to train AI models for predictive analytics, medical diagnosis, and literature summarization. Academic & Scientific Research: Access a vast collection of peer-reviewed studies for literature reviews, meta-analyses, and academic publishing. Regulatory & Compliance Monitoring: Stay updated on medical regulations, FDA approvals, and healthcare policy changes.

    Whether you're conducting medical research, analyzing healthcare trends, or developing AI-driven solutions, our PubMed Dataset provides the structured data you need. Get started today and customize your dataset to fit your research objectives.

  8. r

    Indian Journal of Community Medicine Impact Factor 2024-2025 -...

    • researchhelpdesk.org
    Updated Feb 23, 2022
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    Research Help Desk (2022). Indian Journal of Community Medicine Impact Factor 2024-2025 - ResearchHelpDesk [Dataset]. https://www.researchhelpdesk.org/journal/impact-factor-if/17/indian-journal-of-community-medicine
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    Dataset updated
    Feb 23, 2022
    Dataset authored and provided by
    Research Help Desk
    Description

    Indian Journal of Community Medicine Impact Factor 2024-2025 - ResearchHelpDesk - The Indian Journal of Community Medicine (IJCM), is the official organ & the only official journal of the Indian Association of Preventive and Social Medicine (IAPSM). It is a peer-reviewed journal which is published Quarterly. The journal publishes research articles, focusing on biostatistics, epidemiology, family health care, public health administration, national health problems, medical anthropology, health care delivery and social medicine, invited annotations and comments, invited papers on recent advances, clinical and epidemiological diagnosis and management; editorial correspondence and book reviews. Abstracting and Indexing Information The journal is registered with the following abstracting partners: CNKI (China National Knowledge Infrastructure), Baidu Scholar, EBSCO Publishing's Electronic Databases, Ex Libris – Primo Central, Google Scholar, Infotrieve, Hinari, ProQuest, National Science Library, TdNet, Wanfang Data The journal is indexed with, or included in, the following: Emerging Sources Citation Index, DOAJ, Indian Science Abstracts,MedInd, PubMed Central, IndMed, Scimago Journal Ranking, Web of Science, SCOPUS.

  9. Data from: RELATION BETWEEN IMPACT FACTOR IN ORTHOPEDIC JOURNALS AND LEVEL...

    • scielo.figshare.com
    jpeg
    Updated May 30, 2023
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    Mauricio Pandini Monteiro de Barros; Fabio Teruo Matsunaga; Marcel Jun Sugawara Tamaoki (2023). RELATION BETWEEN IMPACT FACTOR IN ORTHOPEDIC JOURNALS AND LEVEL OF EVIDENCE [Dataset]. http://doi.org/10.6084/m9.figshare.7243985.v1
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    jpegAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Mauricio Pandini Monteiro de Barros; Fabio Teruo Matsunaga; Marcel Jun Sugawara Tamaoki
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    ABSTRACT Objective: This study aims to assess the quality of articles published in the leading orthopedic surgery journals, by measuring the relation between the impact factor and the number studies with a high level of evidence. Methods: A literature review was performed of articles published in four previously selected journals. A score of journal evidence (RER - Relation between Randomized clinical trials and Systematic reviews) was calculated, considering the number of RCTs and SR published and the total number of full-text articles. Results: The selected journals were JBJS-Am, ASMJ, BJJ-Br and Arthroscopy, with Impact factors of 5.280, 4.362, 3.309 and 3.206 respectively in 2015. In the study, the RER Scores, in the same order, were 9.408, 6.153, 7.456 and 7.779. Conclusion: The journal JBJS-Am is the best available source of information on orthopedic surgery from this point of view. It has the highest Impact Factor and clearly the highest RER Score. On the other hand, we could conclude that the number of published RCT and good quality SR is very low, with less than 10% of all the articles. Level of evidence III, Analyses based on limited alternatives and costs, and poor estimates.

  10. f

    Curated citation data

    • fairdomhub.org
    zip
    Updated Jan 11, 2023
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    Sebastian Höpfl (2023). Curated citation data [Dataset]. https://fairdomhub.org/data_files/6184
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    zip(99.4 KB)Available download formats
    Dataset updated
    Jan 11, 2023
    Authors
    Sebastian Höpfl
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The classification in reproducible and not reproducible models was made by Tiwari et al.

    Citations were looked up in Scopus, Web of Science and Google Scholar.

    The following journals had to be excluded, as Journal Impact Factors (JIF) were missing or papers were discontinued: * Experientia was closed 1996 and continued as Cellular and Molecular Life Sciences 1997 * The American journal of physiology – split into fields 1977, further splits in 1980 and 1989 * IFAC Proceedings Volumes – last issue 2014, continued as IFAC-PapersOnLine * Mathematical and Computer Modelling – discontinued as of 2014 * IOP Conference Series: Materials Science and Engineering – not a journal but conference proceedings – no impact factor listed * Infectious Disease Modelling – no impact factor found * Jurnal Teknologi – no impact factor found * JCO clinical cancer informatics – no impact factor found * Quantitative biology (Beijing, China) – no impact factor found * Letters in Biomathematics – no impact factor found * Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference – no impact factor found * Haemostasis – discontinued; no impact factor found

    It was tried to include as many papers as possible.

    As the JIF is calculated every year, an average JIF of the Journal Citation Reports from 2014 to 2021 was calculated and used for the analysis. The results do not differ qualitatively if only the JIF of 2021 was used. As the Journal Impact Factor reports belong to Clarivate the JCR data was not uploaded to the repository.

  11. o

    Data from: Google Scholar as a source for citation and impact analysis for a...

    • explore.openaire.eu
    Updated Jan 1, 2010
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    S. A. Sanni; A. N. Zainab (2010). Google Scholar as a source for citation and impact analysis for a non-ISI indexed medical journal [Dataset]. https://explore.openaire.eu/search/other?orpId=od_124::c09a7638b07750d68773c1f5f9f7b686
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    Dataset updated
    Jan 1, 2010
    Authors
    S. A. Sanni; A. N. Zainab
    Description

    It is difficult to determine the influence and impact of journals which are not covered by the ISI databases and Journal Citation Report. However, with the availability of databases such as MyAIS (Malaysian Abstracting and Indexing System), which offers sufficient information to support bibliometric analysis as well as being indexed by Google Scholar which provides citation information, it has become possible to obtain productivity, citation and impact information for non-ISI indexed journals. The bibliometric tool Harzing's Publish and Perish was used to collate citation information from Google scholar. The study examines article productivity, the citations obtained by articles and calculates the impact factor of Medical Journal of Malaysia (MJM) published between 2004 and 2008. MJM is the oldest medical journal in Malaysia and the unit of analysis is 580 articles. The results indicate that once a journal is covered by MyAIS it becomes visible and accessible on the Web because Google Scholarindexes MyAIS. The results show that contributors to MJM were mainly Malaysian (91) and the number of Malaysian-Foreign collaborated papers were very small (28 articles, 4.8). However, citation information from Google scholar indicates that out of the 580 articles, 76.8 (446) have been cited over the 5-year period. The citations were received from both mainstrean foreign as well as Malaysian journals and the top three citors were from China, Malaysia and the United States. In general more citations were received from East Asian countries, Europe, and Southeast Asia. The 2-yearly impact factor calculated for MJM is 0.378 in 2009, 0.367 in 2008, 0.616 in 2007 and 0.456 in 2006. The 5-year impact factor is calculated as 0.577. The results show that although MJM is a Malaysian journal and not ISI indexed its contents have some international significance based on the citations and impact score it receives, indicating the importance of being visible especially in Google scholar.

  12. Citation and access data, and journal impact factors for co-published...

    • figshare.com
    xlsx
    Updated Jun 1, 2023
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    Daniel Shanahan (2023). Citation and access data, and journal impact factors for co-published EQUATOR reporting guidelines [Dataset]. http://doi.org/10.6084/m9.figshare.3156211.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Daniel Shanahan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is the full citation details and DOIs for 85 co-published reporting guidelines, together with the citation counts, number of article accesses and journal impact factor for each article and journal. This represents a total of nine research reporting statements, published across 58 journals in biomedicine.

  13. m

    Medical Tourism Industry Statistics | Market Size, Share & Growth Rate...

    • mordorintelligence.com
    pdf,excel,csv,ppt
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    Medical Tourism Industry Statistics | Market Size, Share & Growth Rate Forecasts [Dataset]. https://www.mordorintelligence.com/industry-reports/medical-tourism-market
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2021 - 2030
    Area covered
    Global
    Variables measured
    Study Period, Largest Market, CAGR (2025 - 2030), Market Size (2025), Market Size (2030), Market Concentration, Fastest Growing Market
    Description

    How big is the Medical Tourism Market? The Medical Tourism Market size is expected to reach USD 84.92 billion in 2024 and grow at a CAGR of 23.03% to reach USD 239.37 billion by 2029.

       What is the current Medical Tourism Market size? 
       In 2024, the Medical Tourism Market size is expected to reach USD 84.92 billion.
    
       Who are the key players in Medical Tourism Market?
       Healthbase, Apollo Hospitals, KPJ Healthcare, Klinikum Medical Link and Medretreat are the major companies operating in the Medical Tourism Market. 
    
       Which is the fastest growing region in Medical Tourism Market? 
       Asia Pacific is estimated to grow at the highest CAGR over the forecast period (2024-2029). 
    
       Which region has the biggest share in Medical Tourism Market? 
       In 2024, the North America accounts for the largest market share in Medical Tourism Market. 
    
       What years does this Medical Tourism Market cover, and what was the market size in 2023? 
       In 2023, the Medical Tourism Market size was estimated at USD 65.36 billion. The report covers the Medical Tourism Market historical market size for years: 2021, 2022 and 2023. The report also forecasts the Medical Tourism Market size for years: 2024, 2025, 2026, 2027, 2028 and 2029.
    
       What is the dominant segment contributing to the largest market share in Medical Tourism? 
       Cosmetic Treatment is the dominant segment that holds the major share of the Medical Tourism Market.
    
       The Global Medical Tourism Market Report provides a comprehensive industry analysis of the medical tourism market, segmented by treatment type and geography. The market overview highlights the various treatment types including cosmetic, dental, cardiovascular, orthopedics, bariatric, fertility, ophthalmic, and other treatments. The industry statistics indicate significant market growth driven by the increasing demand for affordable and high-quality medical care.<br><br>In terms of market segmentation, the report covers North America, Europe, Asia-Pacific, the Middle East and Africa, and South America, providing a detailed market forecast for each region. The industry size and market value are presented in terms of USD, reflecting the market's economic impact. The market trends and growth rate are analyzed to provide insights into future market predictions.<br><br>The report also includes an industry outlook, focusing on key market leaders and their strategies. The market review highlights the competitive landscape and the role of both private and public healthcare service providers. Additionally, the report examines alternative treatment options and their market share.<br><br>For those seeking more detailed information, the report example and report pdf are available for further industry research. The market data and industry reports offer valuable insights for companies looking to understand the market dynamics and make informed decisions. The industry trends and market outlook provide a clear picture of the market's future direction.<br><br>Overall, the Global Medical Tourism Market Report is an essential resource for understanding the market's growth forecast and industry worth. It provides a thorough market analysis and industry information, making it a valuable tool for research companies and stakeholders in the medical tourism industry.
    
       Medical Tourism Also Known As: Patient Mobility, Transnational Healthcare, Therapeutic Tourism, Medical Vacation, Health Travel
    
       Medical Tourism Report Covers the Following Regions: NA, North America, North American, Northern America, Northern American, EU, Europe, European, APAC, Asia Pacific, Asian, MEA, Middle East and Africa, Middle Eastern and African, MENA, Middle East, Middle Eastern, SA, South America, South American
    
       Medical Tourism Report Covers the Following Countries: USA, United States, US, Canada, Mexican, Mexico, DE, Germany, German, UK, United Kingdom, FR, France, French, IT, Italy, Italian, ES, Spain, Spanish, China, Chinese, JP, Japan, Japanese, IN, India, Indian, AU, Australia, Australian, KR, South Korea, South Korean, GCC, Gulf Cooperation Council, ZA, South Africa, South African, BR, Brazil, Brazilian, AR, Argentina, Argentine
    
  14. Medical_cost_dataset

    • kaggle.com
    Updated Aug 19, 2023
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    Nandita Pore (2023). Medical_cost_dataset [Dataset]. https://www.kaggle.com/datasets/nanditapore/medical-cost-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 19, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Nandita Pore
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Description:

    Explore the intricacies of medical costs and healthcare expenses with our meticulously curated Medical Cost Dataset. This dataset offers valuable insights into the factors influencing medical charges, enabling researchers, analysts, and healthcare professionals to gain a deeper understanding of the dynamics within the healthcare industry.

    Columns: 1. ID: A unique identifier assigned to each individual record, facilitating efficient data management and analysis. 2. Age: The age of the patient, providing a crucial demographic factor that often correlates with medical expenses. 3. Sex: The gender of the patient, offering insights into potential cost variations based on biological differences. 4. BMI: The Body Mass Index (BMI) of the patient, indicating the relative weight status and its potential impact on healthcare costs. 5. Children: The number of children or dependents covered under the medical insurance, influencing family-related medical expenses. 6. Smoker: A binary indicator of whether the patient is a smoker or not, as smoking habits can significantly impact healthcare costs. 7. Region: The geographic region of the patient, helping to understand regional disparities in healthcare expenditure. 8. Charges: The medical charges incurred by the patient, serving as the target variable for analysis and predictions.

    Whether you're aiming to uncover patterns in medical billing, predict future healthcare costs, or explore the relationships between different variables and charges, our Medical Cost Dataset provides a robust foundation for your research. Researchers can utilize this dataset to develop data-driven models that enhance the efficiency of healthcare resource allocation, insurers can refine pricing strategies, and policymakers can make informed decisions to improve the overall healthcare system.

    Unlock the potential of healthcare data with our comprehensive Medical Cost Dataset. Gain insights, make informed decisions, and contribute to the advancement of healthcare economics and policy. Start your analysis today and pave the way for a healthier future.

  15. Data from: Retractions in general and internal medicine in a high-profile...

    • scielo.figshare.com
    jpeg
    Updated Jun 1, 2023
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    Renan Moritz Varnier Rodrigues de Almeida; Fernanda Catelani; Aldo José Fontes-Pereira; Nárrima de Souza Gave (2023). Retractions in general and internal medicine in a high-profile scientific indexing database [Dataset]. http://doi.org/10.6084/m9.figshare.20007037.v1
    Explore at:
    jpegAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Renan Moritz Varnier Rodrigues de Almeida; Fernanda Catelani; Aldo José Fontes-Pereira; Nárrima de Souza Gave
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    CONTEXT AND OBJECTIVE: Increased frequency of retractions has recently been observed, and retractions are important events that deserve scientific investigation. This study aimed to characterize cases of retraction within general and internal medicine in a high-profile database, with interest in the country of origin of the article and the impact factor (IF) of the journal in which the retraction was made. DESIGN AND SETTING: This study consisted of reviewing retraction notes in the Thomson-Reuters Web of Knowledge (WoK) indexing database, within general and internal medicine. METHODS: The retractions were classified as plagiarism/duplication, error, fraud and authorship problems and then aggregated into two categories: "plagiarism/duplication" and "others." The countries of origin of the articles were dichotomized according to the median of the indicator "citations per paper" (CPP), and the IF was dichotomized according to its median within general and internal medicine, also obtained from the WoK database. These variables were analyzed using contingency tables according to CPP (high versus low), IF (high versus low) and period (1992-2002 versus 2003-2014). The relative risk (RR) and 95% confidence interval (CI) were estimated for plagiarism/duplication. RESULTS: A total of 86 retraction notes were identified, and retraction reasons were found for 80 of them. The probability that plagiarism/duplication was the reason for retraction was more than three times higher for the low CPP group (RR: 3.4; 95% CI: [1.9-6.2]), and similar results were seen for the IF analysis. CONCLUSION: The study identified greater incidence of plagiarism/duplication among retractions from countries with lower scientific impact.

  16. Highly cited tropical medicine articles in the early COVID 19 pandemic....

    • zenodo.org
    • explore.openaire.eu
    Updated Apr 11, 2023
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    Yuh-Shan Ho Julián Monge-Nájera; Yuh-Shan Ho Julián Monge-Nájera (2023). Highly cited tropical medicine articles in the early COVID 19 pandemic. Original Excel data on citation and subjects [Dataset]. http://doi.org/10.5281/zenodo.7814224
    Explore at:
    Dataset updated
    Apr 11, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Yuh-Shan Ho Julián Monge-Nájera; Yuh-Shan Ho Julián Monge-Nájera
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ORIGINAL DATA SET FOR STUDY OF PUBLICATION AND CITATION TRENDS, TROPICAL MEDICINE, EARLY COVID 19 PANDEMIC. Background: An adequate response to health needs includes the identification of research patterns about the large number of people living in the tropics and subjected to tropical diseases. Studies have shown that research does not always match the real needs of those populations, and that citation reflects mostly the amount of money behind particular publications. Here we test the hypothesis that research from richer institutions is published in better-indexed journals, and thus has greater citation rates.

    Methods: The data in this study was extracted from the Science Citation Index Expanded database; the 2020 journal Impact Factor (IF2020) was updated to 30 June 2021. We considered places, subjects, institutions and journals.

    Results: We identified 1 041 highly cited articles with 100 citations or more in the category of tropical medicine. About a decade is needed for an article to reach peak citation. Only two Covid-19 related were highly cited in the last three years. Most cited articles were published by the journals Memorias Do Instituto Oswaldo Cruz (Brazil), Acta Tropica (Switzerland), and PLoS Neglected Tropical Diseases (USA). The USA dominated five of the six publication indicators. International collaboration articles had more citations than single-country articles. The UK, South Africa, and Switzerland had high citation rates, as did the London School of Hygiene and Tropical Medicine in the UK, the Centers for Disease Control and Prevention in the USA, and the WHO in Switzerland.

    Conclusions: About ten years of accumulated citations are needed to get 100 citations or more as highly cited articles in the Web of Science category of tropical medicine. Six publication and citation indicators, including authors’ publication potential and characteristics evaluated by Y-index, indicate that the currently available indexing system places tropical researchers at a disadvantage against their colleagues in temperate countries, and suggest that, to progress towards better control of tropical diseases, international collaboration should increase, and other tropical countries should follow the example of Brazil, which provides significant financing to its scientific community.Julián Monge-Nájera1, and Yuh-Shan Ho2*

    1Laboratorio de Ecología Urbana, Vicerrectoría de Investigación, Universidad Estatal a Distancia, 2050 San José, Costa Rica; julianmonge@gmail.com (https://orcid.org/0000-0001-7764-2966)

    *Corresponding author: Trend Research Centre, Asia University, No. 500 Lioufeng Road, Wufeng, Taichung 41354, Taiwan; ysho@asia.edu.tw (https://orcid.org/0000-0002-2557-8736)

  17. Z

    Subject Categories of the twenty journals with the highest Journal Impact...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Aug 6, 2024
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    Herb, Ulrich (2024). Subject Categories of the twenty journals with the highest Journal Impact Factors [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8350
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    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    Herb, Ulrich
    Description

    This table provides information on the subject categories of the 20 Journals with the highest Journal Impact Factors, collection date: 2014-02-17.

  18. Medical Terminology Software Market Analysis, Size, and Forecast 2025-2029:...

    • technavio.com
    Updated Oct 1, 2002
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    Technavio (2002). Medical Terminology Software Market Analysis, Size, and Forecast 2025-2029: North America (US, Canada, and Mexico), Europe (France, Germany, Italy, and UK), APAC (China, India, and Japan), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/medical-terminology-software-market-industry-analysis
    Explore at:
    Dataset updated
    Oct 1, 2002
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Canada, United States, Global
    Description

    Snapshot img

    Medical Terminology Software Market Size 2025-2029

    The medical terminology software market size is forecast to increase by USD 3.8 billion at a CAGR of 26.3% between 2024 and 2029.

    The market is experiencing significant growth, driven primarily by the increasing emphasis on minimizing medical errors and enhancing healthcare efficiency. With the expanding adoption of Healthcare Information and Communication Technology (HCIT), medical terminology software has become an indispensable tool for healthcare providers. However, market expansion is not without challenges. Regulatory hurdles, such as adherence to strict data privacy regulations, impact adoption and necessitate robust security measures. Additionally, supply chain inconsistencies and the need for continuous software updates to maintain accuracy pose challenges.
    Technological innovations, such as artificial intelligence and machine learning, are being integrated into medical terminology software to enhance its capabilities. Despite these obstacles, opportunities abound for companies that can effectively navigate these challenges and offer innovative solutions. By focusing on user-friendly interfaces, seamless integration with existing systems, and robust data security, medical terminology software providers can capitalize on the market's potential for growth.
    

    What will be the Size of the Medical Terminology Software Market during the forecast period?

    Request Free Sample

    In the dynamic US healthcare industry, decision support solutions have emerged as essential tools for healthcare organizations to enhance interoperability and improve patient care. Epic, a leading provider of electronic health records (EHR), is at the forefront of this trend, enabling clinical studies and data aggregation for better quality reporting. Government norms mandate compliance obligations for hospitals and hospital departments, necessitating seamless data integration and interoperability. This is crucial for effective public health surveillance, hospitalizations, and medical billing. Traditional techniques for managing patient data and clinical errors have given way to advanced technologies, including CROs and R&D operations, which prioritize decentralized clinical trials and data aggregation.
    Patient safety concerns, reimbursement, and disparity are significant factors driving the adoption of these technologies. Medical errors, a major concern for patient safety, can be mitigated through EHR and data integration, ensuring accurate claim submissions and condition tracking. Interoperability between healthcare providers plays a vital role in addressing disparities and improving patient epidemiology. The projection period for this market is marked by increasing emphasis on patient safety, government norms, and reimbursement, making it an exciting space for innovation and growth.
    

    How is this Medical Terminology Software Industry segmented?

    The medical terminology software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    End-user
    
      Healthcare providers
      Healthcare payers
      Healthcare IT vendors
    
    
    Type
    
      Services
      Platforms
    
    
    Application
    
      Data integration
      Data aggregation
      Reimbursement
      Clinical trials
    
    
    Geography
    
      North America
    
        US
        Canada
        Mexico
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
    
    
      Rest of World (ROW)
    

    By End-user Insights

    The healthcare providers segment is estimated to witness significant growth during the forecast period. Medical terminology software is a crucial tool for various healthcare organizations, including hospitals, clinics, doctor offices, long-term care facilities, and other healthcare providers. This segment relies heavily on medical terminology software for accurate and consistent clinical documentation, coding, and information sharing. The software is indispensable for healthcare providers, as it streamlines clinical workflows, enhances patient data administration, and supports billing and coding procedures. The market for medical terminology software is witnessing significant advancements, driven by technological innovations, new healthcare solutions, and regulatory compliance obligations. Compliance with government norms, such as interoperability and data integrity, is a major factor propelling the adoption of medical terminology software. Big data derived from EHR systems is transforming healthcare delivery, particularly in managing chronic diseases

    New healthcare solutions, such as Electronic Health Records (EHR) and Decentralized Clinical Trials, are also fueling the demand for medical terminology software. Pricing analysis reveals that medical terminology software is available at various price p

  19. d

    Data from: Criminal Victimization Among Women in Cleveland, Ohio: Impact on...

    • catalog.data.gov
    • icpsr.umich.edu
    Updated Mar 12, 2025
    + more versions
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    National Institute of Justice (2025). Criminal Victimization Among Women in Cleveland, Ohio: Impact on Health Status and Medical Service Usage, 1986 [Dataset]. https://catalog.data.gov/dataset/criminal-victimization-among-women-in-cleveland-ohio-impact-on-health-status-and-medical-s-a5f5e
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justice
    Area covered
    Cleveland, Ohio
    Description

    The impact of criminal victimization on the health status of women is the focus of this data collection. The researchers examined the extent to which victimized women differed from nonvictimized women in terms of their physical and psychological well-being and differences in their use of medical services. The sample was drawn from female members of a health maintenance plan at a worksite in Cleveland, Ohio. Questions used to measure criminal victimization were taken from the National Crime Survey and focused on purse snatching, home burglary, attempted robbery, robbery with force, threatened assault, and assault. In addition, specific questions concerning rape and attempted rape were developed for the study. Health status was assessed by using a number of instruments, including the Cornell Medical Index, the Mental Health Index, and the RAND Corporation test battery for their Health Insurance Experiment. Medical service usage was assessed by reference to medical records. Demographic information includes age, race, income, and education.

  20. i

    Medical AI Data Analysis Market - In-Depth Insights & Analysis

    • imrmarketreports.com
    Updated Feb 2023
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    Swati Kalagate; Akshay Patil; Vishal Kumbhar (2023). Medical AI Data Analysis Market - In-Depth Insights & Analysis [Dataset]. https://www.imrmarketreports.com/reports/medical-ai-data-analysis-market
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    Dataset updated
    Feb 2023
    Dataset provided by
    IMR Market Reports
    Authors
    Swati Kalagate; Akshay Patil; Vishal Kumbhar
    License

    https://www.imrmarketreports.com/privacy-policy/https://www.imrmarketreports.com/privacy-policy/

    Description

    Report of Medical AI Data Analysis Market is covering the summarized study of several factors encouraging the growth of the market such as market size, market type, major regions and end user applications. By using the report customer can recognize the several drivers that impact and govern the market. The report is describing the several types of Medical AI Data Analysis Industry. Factors that are playing the major role for growth of specific type of product category and factors that are motivating the status of the market.

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Jorge H Ramirez (2023). Data (i.e., evidence) about evidence based medicine [Dataset]. http://doi.org/10.6084/m9.figshare.1093997.v24
Organization logo

Data (i.e., evidence) about evidence based medicine

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pngAvailable download formats
Dataset updated
May 30, 2023
Dataset provided by
Figsharehttp://figshare.com/
Authors
Jorge H Ramirez
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

  1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
  2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
  3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
    Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
  4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
  5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
  6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
  7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
  8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
  9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
  10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
  11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
  12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
  13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
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