Selective Aggregation of Self-Disorders in First-Treatment DSM-IV Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
Converging evidence indicates that self-disorders (SDs) selectively aggregate in schizophrenia spectrum conditions. The aim of this study was to test the discriminatory power of SDs with respect to schizophrenia and nonschizophrenia spectrum psychosis at first treatment contact. SDs were assessed in 91 patients referred for first treatment through the Examination of Anomalous Self-experience (EASE) instrument. Diagnoses, symptoms severity, and function were assessed using the Structural Clinical Interview for the DSM-IV, Structured Clinical Interview for the Positive and Negative Syndrome Scale, Calgary Depression Scale for Schizophrenia, Young Mania Rating Scale, and Global Assessment of Functioning-Split Version. Most patients found it highly relevant to talk about SDs. EASE total score critically discriminated between schizophrenia, bipolar psychosis, and other psychoses. The EASE total score was the only clinical measure that showed a significant and robust association with the diagnosis of schizophrenia. Systematic exploration of anomalous self-experiences could improve differential diagnosis in first-treatment patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".