From Experience to Symptoms: A Multilayer Hierarchy of Psychopathological Dimensions in Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: The psychopathology of schizophrenia is a complex amalgamation of features that span across different dimensions. These dimensions range from the experience of altered time and space through self-disorders to perceptual, positive, and negative symptoms. The relationship between these different psychopathological dimensions remains unclear. Addressing this gap was the aim of our study. METHODS: We collected data on schizophrenia spectrum disorder at three medical expert centers, via semi-structured phenomenological interviews, consisting of the Scale for Space and Time Experience in Psychosis (STEP), the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) and, for a subset of these data, the Examination of Anomalous Self-Experience (EASE), and the perceptual domain of the Bonn Scale for the Assessment of Basic Symptoms (BSABS or BONN). Various state-of-the-art statistical methods, including network and mediation analyses, were used to investigate the relationships between these psychopathological dimensions. RESULTS: We found a relationship between altered time and space experiences (STEP) and both general symptoms (PANSS) and the basic self-disorders (EASE). CONCLUSION: Our various network and mediation analyses show that the basic self-disturbance is a key node in mediating the impact of the more fundamental time and space disturbances on both perceptual changes, and negative, positive, and general symptoms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".