Stroke remains a leading cause of mortality and long-term disability worldwide, with ischemic stroke constituting the majority of cases. Despite advances in neuroimaging and acute management, there is a continued need for accessible and reliable biomarkers that may assist in risk stratification, prognostication, and understanding disease mechanisms. Mean platelet volume (MPV), an index of platelet size and activity routinely reported in complete blood counts, has emerged as a potential biomarker in cerebrovascular disease. Larger platelets are metabolically and enzymatically more active, exhibit greater prothrombotic potential, and are associated with enhanced inflammatory responses, all of which are relevant to ischemic stroke pathophysiology.

This article critically examines the biological basis of MPV, its relationship with platelet function, and its association with ischemic stroke severity, subtype, lesion volume, and clinical outcomes. Evidence from epidemiological studies, hospital-based cohorts, and prognostic analyses is synthesized to evaluate whether elevated MPV is associated with worse outcomes in acute ischemic stroke. The interaction of MPV with comorbid conditions such as diabetes mellitus, metabolic syndrome, and cardiovascular disease is also explored. Methodological considerations, including preanalytical and analytical variability in MPV measurement, are discussed as important limitations affecting clinical interpretation.

Overall, current evidence suggests that MPV is associated with stroke risk and may predict severity and functional outcome in selected populations. However, heterogeneity across studies and the absence of standardized cut-off values limit its immediate clinical application. MPV should be viewed as a supportive biomarker that reflects underlying thrombo-inflammatory processes rather than a standalone diagnostic or prognostic tool. Further large-scale, standardized, and prospective studies are required to clarify its role in routine stroke care and to integrate MPV into multimarker approaches for cerebrovascular disease assessment.

Large Language Models (LLMs) have rapidly transitioned from experimental research artifacts to widely deployed socio-technical systems used in journalism, law, education, healthcare, and scientific research. Alongside their demonstrated fluency and versatility, these models exhibit a persistent and consequential limitation: hallucination, broadly understood as the generation of content that is syntactically plausible but factually incorrect, unverifiable, or internally inconsistent. High-profile incidents—including fabricated legal precedents, false accusations against real individuals, and erroneous scientific claims—have underscored the real-world risks associated with unmitigated hallucinations. This article provides a comprehensive, system-level examination of hallucinations in LLMs, synthesizing empirical case studies, theoretical taxonomies, detection methodologies, mitigation strategies, and evaluation frameworks. Drawing on recent academic literature and documented failures in deployed systems, the paper categorizes hallucinations into factual, logical, contextual, and self-contradictory forms, and analyzes their underlying mechanisms in model training, prompting, and deployment contexts. Particular emphasis is placed on black-box and zero-resource detection approaches, including SelfCheckGPT, Chain-of-Thought consistency methods, and alignment-based evaluation metrics such as GPTScore, GEval, and AlignScore. The discussion critically examines the limitations of current benchmarks and the epistemic challenges of evaluating truthfulness in generative systems. The article concludes by outlining open research challenges and ethical considerations, arguing that hallucination mitigation should be framed not as a problem of complete elimination, but as a continuous risk-management process integrating technical, institutional, and human-in-the-loop safeguards.