ArticleIndian journal of thoracic and cardiovascular surgery2025
Correlation and causation for cardiothoracic surgeons: part 4-distinguishing relationships in data.
Article in Indian journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Correlation indicates a relationship between variables without causation, while causation implies one variable directly influences the other in clinical research. Through various statistical approaches, including Pearson and Spearman correlation coefficients, we can explore the strength of linear and non-linear relationships. Phi coefficient and the point-biserial correlation are other alternative techniques. Scatter plots are used to illustrate correlations in real-world data, guiding surgeons in understanding how variables like experience impact complication rates. Emphasis is placed on recognizing confounding variables, applying appropriate statistical methods, and interpreting results accurately to inform clinical decisions. This paper highlights the importance of evidence-based, data-driven practices in enhancing surgical outcomes.
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