text: **JAMA Network Open | Emergency Medicine** Developing and Evaluating LLM-Generated Emergency Medicine Handoff Notes 
  
  LLM-model training, an informatics professional (V.H.) worked over a period of 200 hours with 3 
  
  board certified emergency medicine physician leaders with experience in formal quality and patient 
  
  safety review processes (M.M., A.F., and P.S.) to improve the dataset through manual curation and 
  
  annotation. As the task of EM-handoff note generation is not dependent on racial characteristics of 
  
  the patients, we removed all mentions of race during the annotation stage as a means to avoid race 
  
  bias; therefore, the model was trained to generate text without race-based assumptions. Although 
  
  resource intensive, a small and carefully curated dataset of at least 1000 examples has been shown 
  to be sufficient to produce remarkable results for the language model chosen.42 Given the size of 
  our dataset, we created a train and test dataset with a ratio of 1500:100, with a higher ratio of data 
  
  placed in the training set and eschewed a validation set to lower the variance of the models. We used 
  
  k-fold cross validation on the training dataset to avoid sampling bias for the hyperparameter 
  
  optimization of the LLMs. 