The multidimensional nature of formal thought disorder (FTD) offers potential diagnostic utility in distinguishing between schizophrenia and affective disorders. This study aimed to assess the diagnostic accuracy of the Thought and Language Disorder Scale (TALD) factors in differentiating schizophrenia, mania, depression, and healthy controls, as well as in distinguishing affective and non-affective psychosis. We included 234 participants: Schizophrenia (n = 70), mania (n = 38), depression (n = 71), and healthy controls (n = 55). All participants were assessed using TALD, the Positive and Negative Syndrome Scale (PANSS), the Hamilton Depression Rating Scale (HAMD), and the Young Mania Rating Scale (YMANI). In group comparisons, the mania group showed the highest scores in the Objective Positive and Subjective Positive factors, while the schizophrenia group exhibited global disturbances across all TALD factors. The depression group had lower scores for both Objective Negative and Subjective Negative factors compared to the schizophrenia group. Support Vector Machine models revealed that TALD factors achieved 72% accuracy in classifying psychiatric disorders versus healthy controls, with 95%, 94%, and 77% accuracy in distinguishing schizophrenia from controls, mania, and depression, respectively. Affective and non-affective psychosis were distinguished with 90% accuracy, with affective psychosis showing higher positive FTD scores and non-affective psychosis showing higher negative FTD scores. TALD factors were significantly correlated with core symptom domains of each disorder in PANSS, YMANI, and HAMD scores. The TALD scale demonstrated robust diagnostic utility in differentiating schizophrenia, mania, depression, and healthy controls and in distinguishing between affective and non-affective psychosis. Integrating FTD into diagnostic frameworks, alongside machine learning approaches, may enhance the precision of psychiatric diagnoses.
Mutlu et al. (Fri,) studied this question.