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Background: The periprocedural management of antithrombotic medications in patients with chronic subdural hematoma (cSDH) after middle meningeal artery embolization (MMAE) or surgical evacuation is uncertain.

Methods: A systematic review was conducted across Medline, Embase, and Web of Science databases. We pooled proportions and risk ratios (RRs) for the meta-analysis with the corresponding 95% CIs.

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Background: Neonatal cerebral microbleeds (CMBs) occur infrequently, and during the initial phase, they often present without noticeable clinical symptoms, which can result in delays in both diagnosis and treatment. There has been relatively little research conducted on neonatal CMBs, with even less focus on their related risk factors. However, identifying risk factors and proactively preventing microbleeds is particularly crucial for effective treatment.

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In forensic neuropathology, the β-amyloid precursor protein (β-APP) immunostain is used to diagnose axonal injury (AI). The two most common aetiologies are traumatic (TAI) and ischaemic (vascular; VAI). We aimed to identify background characteristics and neuropathology findings that are suggestive of TAI, VAI, or no AI in neuropathologically examined medico-legal autopsy cases.

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Differentiating benign enlargement of subarachnoid spaces (BESS) from low-attenuation subdural collections on CT imaging of infants can be challenging. This distinction is crucial in infants, as subdural collections may raise the concern for abusive head trauma (AHT). To evaluate the utilization of the displaced cortical vein sign on CT as a predictor of pathological subdural collections confirmed by MRI and to assess the reproducibility of this finding among radiologists with different levels of clinical experience.

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Objective: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to improve patient outcomes. In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.

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