The continuing story of schizophrenia and schizoaffective disorder: One condition or two?
Bibliographic record
Abstract
Although schizophrenia and schizoaffective disorder remain separable in diagnostic systems, the validity of the distinction is uncertain. This study asked whether schizophrenia and schizoaffective disorder are distinguishable on selected cognitive, social cognitive and structural social brain measures. Outpatients with a diagnosis of schizophrenia (n = 44) or schizoaffective disorder (n = 29) and non-psychiatric control participants (n = 62) were studied. Patients were assessed clinically (Positive and Negative Syndrome Scale) and all participants were administered a battery of cognitive (MATRICS Consensus Cognitive Battery; Wechsler Abbreviated Scale of Intelligence, Wide Range Achievement Reading) and social cognitive (Reading the Mind in the Eyes, Mayer-Salovey-Caruso Emotional Intelligence Test; MSCEIT) tasks. In addition, participants underwent structural magnetic resonance imaging (MRI) to yield cortical thickness data for 42 regions associated with the social brain network. Results showed no significant differences between patient groups on 17/18 cognitive/social cognitive and social brain cortical thickness measures. In contrast, schizophrenia and schizoaffective disorder patients differed from controls on 16/18 and 11/18 measures respectively. Schizoaffective disorder patients outperformed schizophrenia patients on an emotion regulation task (MSCEIT). Schizophrenia and schizoaffective disorder are largely indistinguishable on key cognitive, social cognitive and neural measures. The continuing separation of these syndromes in diagnostic systems and disease models requires is questionable and requires further attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".