The Chemistry of Time: Flavor Evolution and Functional Health Benefits in Aged Tea.

People at clinical high risk (CHR) for psychosis, those who meet criteria for attenuated psychosis syndromes, are at greater risk of developing a future threshold psychotic disorder (; ), supporting the utility of early identification of this population to enhance prevention. However, timely determination of whether youth are experiencing these syndromes requires specialized assessments that are not widely () or equitably () available. Further, even when determining that youth are experiencing a CHR syndrome, the majority of these youth will never develop a future threshold psychosis spectrum disorder, with conversion rates averaging around 25% according to a recent meta-analysis (). Regardless of future conversion status, people that meet criteria for a CHR syndrome will tend to have significant and persistent symptom distress and functional impairment even if they no longer experience subthreshold positive symptoms (). More accurately capturing psychosis risk is an ongoing task for psychosis spectrum researchers and clinicians. Established risk metrics, such as those developed by the Shanghai-At-Risk-for-Psychosis (SHARP) () and North American Prodrome Longitudinal Study (NAPLS) () working groups, are invaluable tools and offer comparable accuracy to risk calculators for cardiovascular disease. However, these scores rely on lengthy symptom, functional, and cognitive evaluations by trained assessors. AI-based natural language processing (NLP) tools could bridge some of the gaps in accurate and scalable CHR diagnosis protocols. First, NLP tools can provide novel insights and increase the accuracy of psychosis-risk assessment by tapping into information embedded in unstructured, freely generated assessor notes. Second, the automated nature of these tools offers the potential for cost-effective, scalable assessments that are not bound by the same bottlenecks as traditional tools.