Emerging Treatments for Subarachnoid Hemorrhage.

The current landscape of therapeutic strategies for subarachnoid hemorrhage (SAH), a significant adverse neurological event commonly resulting from the rupture of intracranial aneurysms, is rapidly evolving. Through an in-depth exploration of the natural history of SAH, historical treatment approaches, and emerging management modalities, the present work aims to provide a broad overview of the shifting paradigms in SAH care. By synthesizing the historical management protocols with contemporary therapeutic advancements, patient-specific treatment plans can be individualized and optimized to deliver outstanding care for the best possible SAH-related outcomes.

A scoping review of diet and exercise interventions for older cancer survivors' physical function.

Lifestyle (diet and exercise) interventions across the cancer care continuum among younger cancer survivors (<60 years of age) demonstrate utility in improving physical function, and other cancer relevant health outcomes. However, the impact of lifestyle interventions on physical function in older (≥60 years) cancer survivors is not entirely clear. This scoping review aims to map and characterize the existing literature on the effect of diet and exercise interventions on physical function in older cancer survivors. Conducted to the JBI Manual for Evidence Synthesis and reported to the PRISMA guidelines, the literature search was performed on multiple databases through March 2024. A total of 19,901 articles were identified for screening with 49 articles published between 2006 and 2024 selected for full-text review. Of these, 36 studies included an exercise intervention, two focused on diet intervention, while 11 studies included both diet and exercise intervention. These 49 studies included various cancer types, cancer stages, and timepoints across the cancer care continuum. Most studies described physical function as their primary outcome and demonstrated maintenance or improvement in physical function. We identified several gaps in the current evidence including lack of (adequately powered) trials focused only on older cancer survivors, and trials focused on dietary interventions alone or dietary interventions combined with exercise interventions within this population vulnerable for nutritional inadequacies and declining physical function. Considering the growing population of older cancer survivors, this represents an important area for further research.

Leveraging Natural Language Processing to Extract Features of Colorectal Polyps From Pathology Reports for Epidemiologic Study.

PURPOSE: Histopathologic features are critical for studying risk factors of colorectal polyps, but remain deeply embedded within unstructured pathology reports, requiring costly and time-consuming manual abstraction for research. In this study, we developed and evaluated a natural language processing (NLP) pipeline to automatically extract histopathologic features of colorectal polyps from pathology reports, with an emphasis on individual polyp size. These data were then linked with structured electronic health record (EHR) data, creating an analysis-ready epidemiologic data set. METHODS: We obtained 24,584 pathology reports from colonoscopies performed at the University of Utah's Gastroenterology Clinic. Two investigators annotated 350 reports to determine inter-rater agreement, develop an annotation scheme, and create a reference standard for performance evaluation. The pipeline was then developed, and performance was compared against the reference for extracting polyp location, histology, size, shape, dysplasia, and the number of polyps. Finally, the pipeline was applied to 24,225 unseen reports and NLP-extracted data were linked with structured EHR data. RESULTS: Across all features, our pipeline achieved a precision of 98.9%, a recall of 98.0%, and an F1-score of 98.4%. In patients with polyps, the pipeline correctly extracted 95.6% of sizes, 97.2% of polyp locations, 97.8% of histology, 98.3% of shapes, and 98.3% of dysplasia levels. When applied to unseen data, the pipeline classified 12,889 patients as having polyps, 4,907 patients without polyps, and extracted the features of 28,387 polyps. Tubular adenomas were the most common subtype (55.9%), 8.1% of polyps were advanced adenomas, and the mean polyp size was 0.57 (±0.4) cm. CONCLUSION: Our pipeline extracted histopathologic features of colorectal polyps from colonoscopy pathology reports, most notably individual polyp sizes, with considerable accuracy. This study demonstrates the utility of NLP for extracting polyp features and linking these data with EHR data to create an epidemiologic data set to study colorectal polyp risk factors and outcomes.