Comparison of the ability to mentalization in patients with schizophrenia and schizoaffective psychosis based on the methodology «Understanding the mental state of theeyes»
Bibliographic record
Abstract
We present the data of the survey of patients with schizophrenia, schizoaffective psychosis and a group of mentally healthy people using the test «Understanding the mental state of the eyes» («Eyes test») by Baron-Cohen, aimed at assessing the capacity for mentalization. We describe the procedure for selecting the most valid test cards for Russian sample of subjects. It is shown that the results of the test «Eyes» of schizoaffective disorder patients are intermediate between patients with schizophrenia and healthy subjects, 1. ability for mentalization they save more than patients with schizophrenia. The findings are discussed in the context of theoretical models of the spectrum of mental pathology. The data of the survey of patients and healthy subjects, additional procedures aimed at identifying the communication capacity for mentalization with the severity of psychopathology (Hospital scale questionnaire SCL-90-R) and social motivation in the form of focus on the contact with others and the ability to enjoy them (The scale of social avoidance and distress scale Brief fear of negative evaluation, social anhedonia scale), as well as focus on the mental and emotional sphere of life (Toronto alexithymia scale). Based on the correlation analysis of the data concludes that there is a statistically significant inverse association between the ability to mentalization, on the one hand, and the severity of psychopathology and the reduction of social orientation – on the other
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".