BACKGROUND: Many published accounts of clinical trials report no differences between the treatment arms, while being underpowered to find differences. This study determined how the authors of these reports interpreted their findings. STUDY DESIGN: We examined 54 reports of surgical trials chosen randomly from a database of 110 influential trials conducted in 2008. Seven that reported having adequate statistical power were excluded from further analysis, as were the 32 that reported significant differences between the treatment arms. We examined the remaining (...) 15 to see whether the authors interpreted their negative findings appropriately. Appropriate interpretations discussed the lack of power and/or called for larger studies. RESULTS: Three of the 7 trials that did not report an a priori power calculation offered inappropriate interpretations, as did 3 of the 8 trials that reported an a priori power < 0.90. However, we examined only a modest number of trial reports from 1 year. CONCLUSIONS: Negative findings in underpowered trials were often interpreted as showing the equivalence of the treatment arms with no discussion of the issue of being underpowered. This may lead clinicians to accept new treatments that have not been validated. (shrink)

With the rapid development of science and technology, UAVs have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects (...) of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm, Gradient Boosting Decision Tree algorithm, K Nearest Neighbor algorithm, and Decision Tree algorithm. Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield. (shrink)