Bibliographic record
Abstract
AIM: Emotion recognition is an important aspect of social interactions. Patients suffering from schizophrenia exhibit some disturbances in affective processing. The aim of the study was the evaluation of facial emotion perception and its relation to the psychotic symptoms in schizophrenia patients. METHODS: 102 patients with schizophrenia (F20.0, ICD 10) and 50 healthy volunteers participated in the study; all the subjects were 18-60 years old. Psychical condition was assessed with following diagnostic tools: CGI (Clinical Global Impression Scale), PANSS (Positive and Negative Syndromes Scale), CDSS (Calgary Depression Scale for Schizophrenia), UKU (Side Effect Rating Scale). Facial emotion recognition ability was assessed by SIE-T (Emotional Intelligence Scale - Faces). RESULTS: On the basis of gathered data it was found that patients suffering from schizophrenia performed worse on facial emotion recognition task compared to the healthy subjects. Severity of negative symptoms corresponded with the facial emotion perception impairment. There was no relation found between age of schizophrenia-onset and level of the facial emotion perception impairment, but the facial emotion recognition ability was worsening with the age of the subjects, both healthy and suffering from schizophrenia. CONCLUSIONS: Severity of schizophrenia corresponded with the facial emotion perception impairment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".