text: **Limitations**
  There were several limitations to the study that were primarily driven from constraints of 
  
  infrastructure, as well as regulations, legal governance, and labor requirements. At the study location, 
  
  the data were required to remain on premise at all times and the infrastructure that was provided 
  
  had a GPU limitation of 24 GB. Given these infrastructure restrictions, the best open-source model 
  
  available during the study was LLM 2. Furthermore, we were not able to demonstrate the comparable 
  difference between our fine-tuned LLM 2 model and third party LLMs32,55 because of the study 
  location’s restrictions and concerns with the data retention policies. Nevertheless, our study 
  
  demonstrates the potential capability of integrating state-of-the-art open source LLMs at 
  
  organizations that are less open to integrating third-party LLMs. 
  
  While the dataset was smaller, we made significant efforts to reduce model variance and 
  
  prevent overfitting by allocating more data to the training cohort and using k-fold cross validation. 
  
  And while our ratio split choice implies the testing results will have slightly greater variance than 
  
  expected, this is mitigated through the extensive manual clinical assessment that was performed. 
  
  The study’s multidimensional clinical evaluation was labor intensive, requiring more than 200 hours 