Neurological affection and serum neurofilament light chain in wild type transthyretin amyloidosis.

In contrast to inherited transthyretin amyloidosis (A-ATTRv), neuropathy is not a classic leading symptom of wild type transthyretin amyloidosis (A-ATTRwt). However, neurological symptoms are increasingly relevant in A-ATTRwt as well. To better understand the role of neurological symptoms in A-ATTRwt, A-ATTRwt patients were prospectively characterized at Amyloidosis Center Charité Berlin (ACCB) between 2018 and 2023 using detailed neurological examination, quality of life questionnaires, and analysis of age- and BMI-adapted serum neurofilament light chain (NFL) levels. 16 out of 73 (21.9%) patients presented with a severe neuropathy which we defined by a Neuropathy Impairment Score (NIS) of 20 or more. In this group, quality of life was reduced, peripheral neuropathy was more severe, and spinal stenosis and joint replacements were frequent. Age- and BMI matched serum NFL levels were markedly elevated in patients with a NIS ≥ 20. We therefore conclude that highly abnormal values in neuropathy scores such as the NIS occur in A-ATTRwt, and have an important impact on quality of life. Both peripheral neuropathy and spinal canal stenosis are likely contributors. Serum NFL may serve as a biomarker for neurological affection in patients with A-ATTRwt. It will be important to consider neurological aspects of A-ATTRwt for diagnosis, clinical follow-up, and future treatment development.

[Clinical application of large language models : Does ChatGPT replace medical report formulation? An experience report]. / Anwendung von "large language models" in der Klinik : Ersetzt ChatGPT die Arztbrieferstellung? Ein Erfahrungsbericht.

Artificial intelligence (AI)-based language models, such as ChatGPT offer an enormous potential for research and medical care but also for clinical workflow optimization by making medical documentation easier and more efficient in taking over standardized routine tasks. With their ability to guess a text's content using word statistics and thus outputting contextually relevant results in chat dialogues, large language models (LLM) can provide appropriate summaries of medical documentation for different target groups. For instance, text generation in easy to understand language could potentially contribute to an increase in patients' health literacy and, consequently, to increased adherence to treatment. Subsequent, the function of AI-based chatbot models to improve user experiences and enhance competence in the use of AI-based language models will be adressed. Current limitations and chances in creating epicrises are presented as an experience report. In the future, the implementation of local LLMs in medical management systems (hospital information systems, HIS and practice administration systems, PAS) and in conjunction with the electronic patient records (ePA) can fundamentally change clinical and outpatient care.