[Interpretation of effect measures in clinical research: A practical guide for the clinician]

Modern medical practice faces an ever-growing volume of information that demands accurate statistical interpretation to guide clinical decision-making. Effect measures are essential tools for quantifying the true magnitude of an intervention and assessing its clinical impact. However, limited understanding of these measures among healthcare professionals often hinders the effective translation of evidence into everyday practice. This article's objective is to provide a clear and didactic overview of the principal effect measures used in clinical research, emphasizing their relevance to evidence-based medicine. Key concepts such as the p-value, confidence intervals, relative risk, absolute risk reduction, and number needed to treat are summarized. The p-value reflects statistical significance, but it does not convey the magnitude or clinical importance of an effect. Confidence intervals, on the other hand, offer information about the precision and direction of the estimated effect. Relative risk facilitates the comparison of probabilities between groups, while absolute risk reduction and number needed to treat provide more tangible measures for clinical application. The adequate interpretation of these concepts is a cornerstone of evidence-based medicine, it enables clinicians to critically appraise research findings, and make patient-centered decisions, thereby narrowing the gap between scientific evidence and clinical practice.