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.

High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has emerged as a promising approach for enhancing prognostic accuracy in HGG but this study especially focused on the potential of AI in the recurrence of HGG. A systematic review and meta-analysis were conducted to assess the performance of AI-based models in predicting survival outcomes for HGG patients. Relevant studies were retrieved from PubMed, Embase, Scopus, and Web of Science until 2 Dec 2024, using predefined keywords ("High-Grade Glioma", "Survival" and "Machine Learning") without date or language restrictions. Data extraction and quality assessment were performed in accordance with PRISMA and PROBAST guidelines. In this study were included. The pooled diagnostic metric, the area under the curve (AUC), was analyzed using random-effects models. A total of 39 studies with 29 various algorithms and 79,638 patients were included, with 15 studies contributing to the meta-analysis. The most commonly used algorithms were random forest (RF) and logistic regression (LR), which demonstrated robust predictive accuracy. The pooled AUCs for one-year, two-year, three-year and overall survival predictions were 0.816, 0.854, 0.871 and 0.789 respectively. Subgroup analysis revealed that RSF achieved the highest predictive accuracy with an AUC of 0.91 (95% CI: 0.84-0.98), while LR followed with an AUC of 0.89 (95% CI: 0.82-0.96). Models integrating clinical, radiomics, and genetic features consistently outperformed single-data-type models. MRI was the most frequently utilized imaging modality. AI-based models, particularly ML and DL algorithms, show significant potential for improving survival prediction in HGG patients. By integrating multimodal data, these models offer valuable tools for personalized treatment planning, although further validation in prospective, multicenter studies is needed to ensure clinical applicability.