Anesthetic Management of a Patient With Juvenile Hyaline Fibromatosis: A Case Report Written With the Assistance of the Large Language Model ChatGPT

This case report was written with the assistance of the large language model known as ChatGPT, a form of generative artificial intelligence that can write grammatically correct and semantically meaningful prose on a multitude of topics. Here, it has assisted us in presenting a case of anesthetic management for a case of Juvenile Hyaline Fibromatosis (JHF), an extremely rare genetic disorder that is part of a spectrum of diseases presently characterized as Hyaline Fibromatosis Syndrome (HFS), which also includes a more severe variant presenting in infancy. HFS is caused by autosomal recessive mutations in the ANTXR2 (anthrax toxin receptor cell adhesion molecule 2) gene, which binds collagen IV and laminin, suggesting that it may be involved in extracellular matrix adhesion. Defects in this molecule lead to abnormal deposition of hyaline material in perivascular areas, presenting as cutaneous lesions, joint contractures, and in some cases internal organ dysfunction. Anesthetic management of patients with JHF may present difficulties with patient positioning and airway management. Most reports of anesthetic management concern children with severe disease and adult reports are uncommon. We present a case of JHF in a 39-year-old woman managed for resection of a lower extremity cutaneous lesion. The anesthetic management of this relatively minor case was uneventful, but the process of drafting this report with the assistance of the new software tool ChatGPT was informative of both its strengths and limitations.