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When multiple treatments are available, network meta-analysis can evaluate data to rank the relative effectiveness. We applied this approach to first-line treatments for paroxysmal atrial fibrillation (medical therapy, radiofrequency ablation or cryoballoon ablation). Individual trials were analysed based on the restricted mean survival time (RMST). Randomised controlled trials (RCT) assessing first-line treatments for paroxysmal atrial fibrillation were referenced from PubMed and the websites of regulatory agencies. The primary end-point was atrial fibrillation recurrence-free survival at 12 months. The treatments assessed for their relative effectiveness were medical therapy, radiofrequency ablation and cryoballoon ablation. Individual trials were examined based on RMST. A Bayesian network meta-analysis was conducted to comparatively evaluate these treatments. Five trials were included in the analysis: two compared radiofrequency with medical treatment and three cryoballoon ablation with medical treatment. The indirect comparison of radiofrequency ablation vs cryoballoon ablation was assessed in the absence of RCTs. Differences in RMST (with 95% credible intervals) were estimated for all binary comparisons (direct or indirect). Radiofrequency and cryoballoon ablation showed significantly increased effectiveness compared with medical treatment. In the indirect comparison, radiofrequency showed a non-significant advantage over cryoballoon ablation. The ranking of effectiveness was as follows: (1) radiofrequency; (2) cryoballoon ablation; (3) medical treatment. In conclusion, we found that radiofrequency was the most effective treatment for paroxysmal atrial fibrillation according to a Bayesian probabilistic model., Competing Interests: The authors declare no conflict of interest., (© 2021 The Author(s). Published by IMR Press.)

A "simplified" figure was proposed in 2011 to summarize the results of controlled trials that evaluate different treatments aimed at the same disease condition. The original criteria for classifying individual binary comparisons included superiority, inferiority and no significance difference; hence, they did not differentiate between no proof of difference vs proof of no difference. We updated the criteria employed in the original "simplified" figure in order to include this differentiation. A revised version of the simplified figure is proposed and described herein. An example of application is also presented. The example is focused on first-line treatments for paroxysmal atrial fibrillation. Three treatments (medical therapy, cryoballoon ablation, radiofrequency ablation) are compared with one another through direct and indirect comparisons., Competing Interests: Conflict-of-interest statement: The authors declare no conflicts of interests., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)

Parkinson's Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson's patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson's, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson's Disease.