Transparent Reporting of Statistics in Surgery (TRESS): A Framework for Clinical Interpretability

In surgical research, statistical sophistication is too often mistaken for scientific rigor. Across a growing body of plastic surgery literature, adjusted odds ratios, hazard ratios, and regression coef- ficients are frequently presented without the crude event rates or absolute measures of effect that give findings clinical meaning. We describe this phenomenon as "runic statistics": results that are statistically valid yet clinically opaque. Through examples drawn from contemporary plastic surgery studies, we highlight three recurrent interpretive flaws: reliance on statistical significance without consideration of clinical relevance, reporting of relative measures without baseline risks or absolute differences, and conflation of association with causation. We further demonstrate how case-mix imbalances can create apparent contradictions in results (Simpson's paradox), and how identical odds ratios can translate into very different clinical implications depending on the base- line risk. To address these challenges, we propose a thirteen-step reporting framework designed to promote transparency, interpretability, and clinical applicability. Key elements include explicit definition of the estimand, presentation of both crude and adjusted data, translation of relative effects into absolute risks and patient-facing numbers, assessment of minimal clinically important differences, careful handling of confounding, and restraint in the use of causal language. By an- choring statistical reporting in clinical realities, surgical research can remain both methodologically rigorous and directly relevant to patient care. Our goal is not to simplify science, but to ensure that its communication is clear, transparent, and ultimately useful at the bedside.