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While results are promising, the product still lacks peer-reviewed clinical data and formal regulatory approval, so users are advised to consult qualified practitioners and maintain realistic expectations

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Our results suggest that SGB is appropriate to classify narcolepsy subtypes with a high accuracy, and it can consider mixture of clinical features on machine scale and learn to classify NT1 and NT2 by identifying the most important features that are even not part of the current classification criteria such as the mean REM sleep latency of MSLT (which indicates no circular reasoning)

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