Key Highlights
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Large language models (LLMs) used to screen unstructured clinical notes can identify epilepsy surgery candidates with near-perfect sensitivity. This suggests that off-the-shelf AI tools can help address the severe under-referral of eligible patients by flagging those not previously considered for surgery in routine care.
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In a study of 110 patients, 45% of those meeting surgical criteria had no prior consideration of surgery in their medical records. The findings highlight a critical gap in care that can be closed using automated screening of routine clinical notes.
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Acute sleep restriction in chronically stressed mice produces behavioral changes that are bidirectionally modulated by the drug L-NAME, as assessed using a hole-board analysis. This research helps clarify the complex interactions between stress, sleep loss, and brain function, which may inform future treatments for neurodevelopmental and mood disorders.
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