This paper puts forward a syntactic account for the evolution of pragmatic expressions with a focus on sentence adverbs and modal particles. These go back to very different sources, ranging from adjectives or adverbs to focus particles to fully-fledged finite or non-finite clauses. The paper’s main aim is to identify the major characteristics and the driving force behind these developments.The proposal is built on recent synchronic approaches positing a syntactically encoded layer in sentential architecture which hosts projections related to evidential or epistemic speaker evaluation, to properties of the speech act and the like (e.g. Krifka 2023). It will be argued that it is this layer that propels pertinent diachronic processes of ‘pragmaticalization’ along different pathways. Based on Diewald’s (2011) notion of pragmaticalization as grammaticalization of discourse functions, pragmaticalization is defined syntactically as grammaticalization into this higher functional layer. This way, the key characteristics of the process and its differences to classical grammaticalization can be accounted for.

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63-0.89], specificity of 0.78[95% CI: 0.68-0.85], positive DLR of 3.54 [95% CI: 2.22-5.64] and the negative DLR of 0.28 [95% CI: 0.15-0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66-35.28], the diagnostic score of 2.55 [95% CI: 1.54-3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings.

To provide an overview of the predictive value of A Body Shape Index (ABSI) for the risk of cardiovascular events.

Ectomesenchymal chondromyxoid tumor (ECT) is a rare benign intraoral tumor that almost exclusively presents as a small mass on the anterior dorsal tongue. Recently, the ras-responsive element-binding protein 1::myocardin-related transcription factor B (RREB1::MRTFB; previously known as MKL2) fusion gene has been identified in 90% of ECTs, all localized to the tongue, highlighting its genetic distinctiveness. Herein, we report a mesenchymal tumor involving the plantar fascia of the left foot in a young woman, harboring the RREB1::MRTFB fusion gene.