Background: Peripheral neuropathy can significantly impact the quality of life for those who are affected, as therapies from the current treatment algorithm often fail to deliver adequate symptom relief. There has, however, been an increasing body of evidence for the use of cannabinoids in the treatment of chronic, noncancer pain. The efficacy of a topically delivered cannabidiol (CBD) oil in the management of neuropathic pain was examined in this four-week, randomized and placebocontrolled trial. Methods: In total, 29 patients with symptomatic peripheral neuropathy were recruited and enrolled. 15 patients were randomized to the CBD group with the treatment product containing 250 mg CBD/3 fl. oz, and 14 patients were randomized to the placebo group. After four weeks, the placebo group was allowed to crossover into the treatment group. The Neuropathic Pain Scale (NPS) was administered biweekly to assess the mean change from baseline to the end of the treatment period. Results: The study population included 62.1% males and 37.9% females with a mean age of 68 years. There was a statistically significant reduction in intense pain, sharp pain, cold and itchy sensations in the CBD group when compared to the placebo group. No adverse events were reported in this study. Conclusions: Our findings demonstrate that the transdermal application of CBD oil can achieve significant improvement in pain and other disturbing sensations in patients with peripheral neuropathy. The treatment product was well tolerated and may provide a more effective alternative compared to other current therapies in the treatment of peripheral neuropathy.

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Large transformer-based language models have achieved incredible success at various tasks which require narrative comprehension, including story completion, answering questions about stories, and generating stories ex nihilo. However, due to the limitations of finite context windows, these language models struggle to produce or understand stories longer than several thousand tokens. In order to mitigate the document length limitations that come with finite context windows, we introduce a novel architecture that augments story processing with an external dynamic knowledge graph. In contrast to static commonsense knowledge graphs which hold information about the real world, these dynamic knowledge graphs reflect facts extracted from the story being processed. Our architecture uses these knowledge graphs to create information-rich prompts which better facilitate story comprehension than prompts composed only of story text. We apply our architecture to the tasks of question answering and story completion. To complement this line of research, we introduce two long-form question answering tasks, LF-SQuAD and LF-QUOREF, in which the document length exceeds the size of the language model's context window, and introduce a story completion evaluation method that bypasses the stochastic nature of language model generation. We demonstrate broad improvement over typical prompt formulation methods for both question answering and story completion using GPT-2, GPT-3 and XLNet.

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