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. Author manuscript; available in PMC: 2022 Nov 20.
Published in final edited form as: Schizophr Res. 2022 Feb 7;242:73–77. doi: 10.1016/j.schres.2022.01.053

Table 2.

Main clinical applications of HiTOP reconceptualization of schizophrenia.

Clinical
considerations
Traditional diagnosis HiTOP model
General features
 Reliability kappa =0.46 for schizophrenia (Regier et al., 2013) ICC = 0.81 for psychoticism, ICC = 0.85 for detachment (Markon et al., 2011)
 Validity Original motivation for schizophrenia was to identify cases with poor prognosis, high impairment, and distinct etiology (Jablensky, 2007) Appears to be twice more informative than psychotic disorder diagnosis regarding prognosis, community functioning, and neurobiology (Kotov et al., 2020; Martin et al., 2021)
 Clinical Utility Efficiently conveys key clinical information with one term. Diagnosis is used more for administrative requirements than treatment decisions (First et al., 2018) Surveys of clinicians indicate greater utility of dimensional than categorical nosology: robust evidence for personality disorders, emerging evidence for psychotic disorders (Bornstein and Natoli, 2019; Mościcki et al., 2013)
Applications
 Risk assessment/Prevention Attenuated psychosis syndrome is included as a condition for further study Promises detailed description of risk as elevations on dimensions. It is compatible with clinical high risk and staging models
 Diagnosis List of categorical descriptors, severity specifiers are available for some disorders Patient’s profile across dimensions. Mild, moderate, and marked degree of elevation are indicated on the profile
 Treatment selection Existing treatments are approved for traditional diagnoses. Usually provides only one threshold to guide all clinical decisions Aligned with the common practice of treating symptoms; offers to formalize and supports this approach; also will ultimately allow multiple thresholds tailored to particular clinical actions
 Tracking treatment progress Includes criteria for remission Progression can be tracked as continuous trajectories rather than transition over an arbitrary threshold

ICC = intraclass correlation coefficient.