Analysis of medical publications from the standpoint of evidence-based medicine. Basic statistical knowledge needed to interpret evidence-based medicine data.
Year: 2025
Type: article
Abstract: As eras pass, communities grow, people prosper and man improves his conceptual knowledge; the need to match that innovation and use the up-to-date scientific methods to choose the best healthcare decisions is of utmost importance. Started as a movement in the 1990s has now become a golden reference method to combine expertise of people all over and provide guidance for clinical methods and improve medical practice. This helps reduce errors in provision of healthcare which result in optimal clinical care. Evidence-based medicine emphasizes the use of the best available evidence available in clinical analysis to create the highest-yield results,, which in this case is the optimal clinical care. EBM follows the latest medical publications and rates/determines their validity, relevance, and quality. This serves a solid ground to create accurate and informed decisions. Professionals interpret the data provided in given publications and researches. This evaluation requires critical understanding of statistics, as it forms the backbone of most clinical researches. This article will explore the process of analyzing medical publications in EBM, particularly focusing on statistical knowledge needed to interpret data effectively. Keywords: EBM (evidence-based medicine), publications, reviews, analysis, evaluation, statistical, interpretation, reliability, judgement, meta-analyses, systematic reviews, cohort studies, clinical trials, p-value, bias, hypothesis, CI (confidence intervals), effect size, randomization, continuous, categorical, Egger’s regression, Rosenthal’s fail-safe number, Cohen’s D, RR (relative risk), AR (absolute risk), OR (odd ratio), RD (risk difference)
Authors: Abdilazizova Asema R., Ahmad Azeem, Khan Mohammad Zeeshan
Institution: Osh State University
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893285189
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