The most dangerous error in clinical trial interpretation is equating p > 0.05 with no effect. This review provides a practical, algorithm-based framework for classifying randomized controlled trial (RCT) results into six distinct categories positive, imprecise (+), neutral, inconclusive, negative, and harmful using confidence interval (CI) position relative to the minimal clinically important difference (MCID) as the primary tool, augmented by Bayesian posterior probabilities. We demonstrate that the same p > 0.05 result can represent three fundamentally different conclusions (inconclusive, negative, or neutral), show how Bayesian reanalysis can rescue benefit signals missed by frequentist thresholds, and illustrate the framework with real-world examples from critical care and cardiology trials. The framework synthesizes guidance from Altman, Harrell, Pocock, Zampieri, the ASA, and ICH E9 into a single coherent decision algorithm.

翻译：临床解读中最危险的错误是将p > 0.05等同于无效应。本文提供了一个基于算法的实用框架，将随机对照试验（RCT）结果分为六类明确的类别：阳性、不精确（+）、中性、不确定、阴性和有害。该框架以置信区间（CI）相对于最小临床重要差异（MCID）的位置作为主要工具，并辅以贝叶斯后验概率。我们证明，相同的p > 0.05结果可能代表三种根本不同的结论（不确定、阴性或中性），展示了贝叶斯再分析如何能够恢复频率学派阈值所遗漏的获益信号，并通过重症监护和心脏病学试验中的真实案例阐明了这一框架。该框架综合了Altman、Harrell、Pocock、Zampieri、美国统计协会及ICH E9的指南，形成了一套统一的决策算法。