Geographical distribution of clinical trials in amyotrophic lateral sclerosis: a scoping review.

Introduction: Clinical trials location is determined by many factors, including the availability of patient populations, regulatory environment, scientific expertise, and cost considerations. In clinical drug development of amyotrophic lateral sclerosis (ALS), where genetic differences have been described and may be related to geographic setting, this could have implications for the clinical interpretation of results in underrepresented geographic settings. Objective: The aim of this study was to review country participation in ALS clinical research based on available data from clinical trial registries and databases. Methods: We performed a scoping review with available information about clinical trials on ALS in ClinicalTrials.gov (CT), EU clinical trials register (EudraCT), WHO International Clinical Trials Registry Platform (ICTRP) and Web of Science (WOS). Inclusion criteria were clinical trials in phase 2 and 3 to treat ALS, recruiting or active not recruiting, from 23/06/2018 to 23/06/2023. Results: The total number of clinical trials identified were 188; 54 studies in CT, 38 in EudraCT, 47 in ICTRP and 49 in WOS. We identified 77 clinical trials after deleting duplicates and applying exclusion criteria. The countries with most studies conducted were the US with 35 studies (10.9%), followed by the United Kingdom, Belgium, France and Germany with 21 studies each one of them (6.5%). Conclusion: The data obtained in our review showed a non-homogeneous distribution in clinical trials at the international level, which may influence the interpretation of the results obtained.

The Effect of the Drug-Related Problems Prevention Bundle on Early Readmissions in Patients From the Emergency Department: A Randomized Clinical Trial.

BACKGROUND: Drug-related problems (DRPs) are prevalent and avoidable disease that patients experience due to drug use or nonuse. However, secondary prevention policies have not yet been systematized. OBJECTIVE: To assess the clinical impact of a secondary prevention bundle for DRPs in patients who visited the emergency department (ED) for medicine-related problems. METHODS: A single-center randomized clinical trial was conducted from August 28, 2019, to January 28, 2021, with 1-month follow-up. We included 769 adult patients who visited ED with a DRP associated with cardiovascular, alimentary tract, and metabolic system medications. For the intervention group, a DRP prevention bundle, consisting of a combined strategy initiated in the ED was applied. Patients in the control group received standard pharmaceutical care. Intervention was evaluated in terms of 30-day hospital readmission due to any cause. RESULTS: Final analysis included 769 patients, of which 68 (8.8%) were readmitted within 30 days (control group, 40 of 386 [cumulative incidence: 10.4%]; intervention group, 28 of 383 [cumulative incidence, 7.3%]). After adjustment of the model for chronic heart failure, there was a lower incidence of hospital readmission among patients in the intervention group compared with those in the control group, odds ratio: 0.59 [95% confidence interval: 0.37-0.97]; number needed to treat (NNT) = 32. No significant differences in other outcomes were observed. CONCLUSION AND RELEVANCE: In this clinical trial, DRP prevention bundle in adjusted analysis decreased the rate of 30-day hospital readmission for any cause in patients who visited ED for a DRP. TRIAL REGISTRATION: ClinicalTrials.gov (Identifier: NCT03607097).

Accuracy-based learning classifier systems: models, analysis and applications to classification tasks.

Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.