This paper reports preliminary results on the impact of different configurations of clinical text in the quality of the outputs of language models for the task of ICD code assignment. A novel dataset of hospital discharge texts in Spanish extracted over a five-year period from a large healthcare institution is used for the evaluation. The dataset comprises over 142,000 clinical reports, each combining multiple text fields from discharge forms. Different text configurations including full text and specific sections such as, diagnostic orientation, chief complaint and exploration notes. Several pretrained BERT-based models were fine-tuned on these configurations to analyze the effect of context length on classification performance. The results suggest that training BERT models with the full text may not be necessary to achieve good results for ICD Classification.