BACKGROUND: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals.
OBJECTIVE: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance.
METHODS: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance.
RESULTS: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD.
CONCLUSIONS: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes.
UNASSIGNED: PRR1-10.2196/48156.

Fine flavor cocoa (FFC) is known for its unique flavor and aroma characteristics, which vary by region. However, a comprehensive overview of the common sensory attributes used to describe FFC beans and chocolate is lacking. Therefore, a systematic review was conducted to analyze existing literature and identify the most commonly used sensory attributes to describe FFC beans and chocolate. A systematic search of the Web of Science and Scopus databases was conducted in May 2023, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to ensure transparency and reproducibility. This review summarizes the origins of cocoa and explores their unique flavor profiles, encompassing caramel, fruity, floral, malty, nutty, and spicy notes. Although some origins may exhibit similar unique flavors, they are often described using more specific terms. Another main finding is that although differences in sensory attributes are anticipated at each production stage, discrepancies also arise between liquor and chocolate. Interestingly, fine chocolate as the final product does not consistently retain the distinctive flavors found in the liquor. These findings emphasize the need for precise descriptors in sensory evaluation to capture flavor profiles of each origin. As such, the exploration of attributes from bean to bar holds the potential to empower FFC farmers and chocolate producers to effectively maintain quality control.

BACKGROUND: Understanding how biological communities change over time is of increasing importance as Earth moves into the Anthropocene. A wide variety of methods are used for multivariate community analysis and are variously applied to research that aims to characterise temporal dynamics in community composition. Understanding these methods and how they are applied is useful for determining best practice in community ecology.
METHODS: We reviewed the ecological literature from 1990 to 2018 that used multivariate methods to address questions of temporal community dynamics. For each paper that fulfilled our search criteria, we recorded the types of multivariate analysis used to characterise temporal community dynamics in addition to the research aim, habitat type, location, taxon and the experimental design.
RESULTS: Most studies had relatively few temporal replicates; the median number was seven time points. Nearly 70% of studies applied more than one analysis method; descriptive methods such as bar graphs and ordination were the most commonly applied methods. Surprisingly, the types of analyses used were only related to the number of temporal replicates, but not to research aim or any other aspects of experimental design such as taxon, or habitat or year of study.
CONCLUSIONS: This review reveals that most studies interested in understanding community dynamics use relatively short time series meaning that several, more sophisticated, temporal analyses are not widely applicable. However, newer methods using multivariate dissimilarities are growing in popularity and many can be applied to time series of any length.

Growing demand for sustainable food has led to the development of meat analogs to satisfy flexitarians and conscious meat-eaters. Successful combinations of functional ingredients and processing methods result in the generation of meat-like sensory attributes, which are necessary to attract non-vegetarian consumers. Sensory science is a broader research field used to measure and interpret responses to product properties, which is not limited to consumer liking. Acceptance is evaluated through hedonic tests to assess the overall liking and degree of liking for individual sensory attributes. Descriptive analysis provides both qualitative and quantitative results of the product\'s sensory profile. Here, original research papers are reviewed that evaluate sensory attributes of meat analogs and meat extenders through hedonic testing and/or descriptive analysis to demonstrate how these analytical approaches are important for consumer acceptance. Sensory evaluation combined with instrumental measures, such as texture and color, can be advantageous and help to improve the final product. Future applications of these methods might include integration of sensory tests during product development to better direct product processing and formulation. By conducting sensory evaluation, companies and researchers will learn valuable information regarding product attributes and overall liking that help to provide more widely accepted and sustainable foods.

UNASSIGNED: This study aimed to systematically evaluate the effects of constraint-induced aphasia therapy (CIAT) for aphasic patients reported by randomized controlled trials.
UNASSIGNED: Relevant randomized controlled trials were retrieved from 11 electronic databases. A methodological quality assessment was conducted in accordance with the Cochrane Handbook, and meta-analyses were performed by using RevMan 5.2. A descriptive analysis was conducted when the included trials were not suitable for a meta-analysis.
UNASSIGNED: A total of 12 trials were included. A statistically significant group difference was shown from the meta-analysis in the results measured by the Western Aphasia Battery (random-effects model, MD = 1.23, 95% CI = 0.31 to 2.14, P < 0.01). However, there were no statistically significant differences shown in the results of the Boston Naming Test (fixed-effects model, MD = -1.79, 95% CI = -11.19 to 7.62, P > 0.05) and Aachen Aphasia Test (fixed-effects model, MD = -1.11, 95% CI = -4.49 to 2.27, P > 0.05). The descriptive analysis showed positive results in language performances of naming, repetition, and comprehension.
UNASSIGNED: This systematic review indicated that CIAT was efficient for improving language performance with regard to naming, comprehension, repetition, written language, and oral language based on the current evidence. And this review provides some meaningful guides for clinical practice: expand the therapy duration to 2 or 3 h per day, focus on naming, and choose the best assessment tool. It also indicates a need for more rigorous, large-scale, and high-quality trials in the future.

As globalization progresses, consumers are readily exposed to many foods from various cultures. The need for studying specialty and unique food products, sometimes known as traditional, authentic, ethnic, exotic, or artisanal foods, is increasing to accommodate consumers\' growing demands. However, the number of studies conducted on these types of products with good quality sensory testing is limited. In this review, we analyzed and reviewed sensory and consumer research on specialty and unique food products. Various factors such as manufacturing, processing, or preparation methods of the samples influence the characteristics of food products and their acceptability. Sensory descriptive analysis can be used to distinguish characteristics that highlight these differences, and consumer research is used to identify factors that affect acceptability. Familiarity with product attributes contributes to consumer acceptance. When cross-cultural consumer research is conducted to support product market placement and expansion, sensory descriptive analysis should be conducted in parallel to define product characteristics. This allows better prediction of descriptors that influence consumer acceptability, leading to appropriate product modification and successful introduction.