This study, by Cao et al., aims to create a depression dataset that accurately represents a clinical environment. Integrating audio recordings and transcriptions of patient consultations, Hamilton Depression Rating Scale (HAMD-17) scores, and qualitative descriptions of patient emotions, the researchers created a novel dataset and analyzed its features based on features in the audio recording, textual content, and HAMD-17 component aspects. Furthermore, the researchers analyzed the usage of different LLMs in predicting the HAMD-17 score in each depression attribute measured, and they found that LLM results often deviated from physician assessments with only an approximately 0.4 - 0.6 F1 score for each HAMD-17 attribute. It was also found that including a text transcription of the recordings and patient emotions with the training data led to a significant increase in diagnostic accuracy. Finally, the researchers compared their new dataset to a pre-existing dataset called MODMA in terms of diagnostic capabilities. From their results, it was demonstrated that while the prediction accuracy was lower for the new dataset, MODMA included a shorter audio length for each patient, which causes it to be less representative of real patient data.