Facial train-of-four monitoring as an evaluation of neuromuscular blockade in a patient with ICU-acquired weakness

Neuromuscular blocking agents are used in the ICU for various reasons, such as during status asthmaticus and patient-ventilator dyssynchrony. We report a 76-year-old man with adenocarcinoma of the oesophagus treated with laparoscopic transthoracic oesophagectomy, which was complicated by a fistula between the gastric reconstruction and the right main bronchus. He developed extensive ICU-acquired weakness and was treated with differential lung ventilation followed by continuous rocuronium infusion. Evaluation of neuromuscular blockade by train-of-four (TOF) stimulation showed a discrepancy in facial and ulnar TOF monitoring. The different number of neuromuscular junctions at each muscle group could be an explanation for this. Therefore, it is suggested to use facial TOF monitoring in ICU patients instead of ulnar TOF monitoring to differentiate between an intoxication of neuromuscular blockade and ICU-acquired weakness

The Theoretical and Statistical Ising Model: A Practical Guide in <i>R</i>

The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options

The validity of the tool “statcheck” in discovering statistical reporting inconsistencies

The R package “statcheck” (Epskamp & Nuijten, 2016) is a tool to extract statistical results from articles and check whether the reported p-value matches the accompanying test statistic and degrees of freedom. A previous study showed high interrater reliabilities (between .76 and .89) between statcheck and manual coding of inconsistencies (.76 - .89; Nuijten, Hartgerink, Van Assen, Epskamp, & Wicherts, 2016). Here we present an additional, detailed study of the validity of statcheck. In Study 1, we calculated its sensitivity and specificity. We found that statcheck’s sensitivity (true positive rate) and specificity (true negative rate) were high: between 85.3% and 100%, and between 96.0% and 100%, respectively, depending on the assumptions and settings. The overall accuracy of statcheck ranged from 96.2% to 99.9%. In Study 2, we investigated statcheck’s ability to deal with statistical corrections for multiple testing or violations of assumptions in articles. We found that the prevalence of corrections for multiple testing or violations of assumptions in psychology was higher than we initially estimated in Nuijten et al. (2016). Although we found numerous reporting inconsistencies in results corrected for violations of the sphericity assumption, we demonstrate that inconsistencies associated with statistical corrections are not what is causing the high estimates of the prevalence of statistical reporting inconsistencies in psychology