Predicting Ordinal Clinical Outcomes in At-Risk Mental States Using Multimodal Factors

Eighty-seven subjects with at-risk mental states (ARMS) were followed up, with clinical outcomes classified into four ordered categories. The dataset contains baseline measures for 15 explanatory variables, including clinical symptoms, cognitive functioning, and electrophysiological measures like P300 and mismatch negativity. The data was authored by Kazuya Nagasawa and last updated on 2026-05-28.

Use Cases

• Predicting multilevel clinical outcomes based on integrated neurobiological, clinical, and functional markers.
• Identifying electrophysiological predictors, such as mismatch negativity amplitude, for worse ordered outcomes.
• Stratifying individuals at the ARMS stage for personalized intervention planning based on baseline symptom severity and cognitive functioning.

Strengths

• Includes 15 baseline explanatory variables spanning clinical, functional, and electrophysiological domains.
• Follow-up data for 87 subjects with at-risk mental states, classified into four ordered outcome categories.
• Uses a validated framework derived from the North American Prodrome Longitudinal Study 2 (NAPLS-2).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 43.2 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Clinical study with baseline and follow-up assessments of subjects with at-risk mental states.

Freshness

Last updated 2026-05-28 06:17:31; freshness should be verified.