Clinical symptomatology and facial emotion recognition in schizophrenia: Which relationship?
Bibliographic record
Abstract
Introduction Patients with schizophrenia show impairments in social cognitive abilities, such as recognizing facial emotions. However, the relationships between specific deficits of emotion recognition and with clusters of psychotic remain unclear. Objectives To explore whether facial emotion recognition was associated with severity of symptoms and to which presentation of psychotic symptoms. Methods Facial emotion recognition (FER) were evaluated in 58 patients with stable schizophrenia with a newly validated FER task constructed from photographs of the face of a famous Tunisian actress representing the Ekman's six basic emotions (happiness, anger, disgust, sadness, fear, and surprise). Symptomatology evaluation comprised the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Clinical Global Impressions Scale Improvement and severity (CGI). Results Patients who failed to identify anger had significantly higher scores in hyperactivity item (P < 0.0001). The patients who had a difficulty to identify sadness had more grandiosity (P ≤ 0.002). The impairment in happiness recognition was correlated with hallucination (P = 0.007) and delusion (P = 0.024) items. Incapacity to identify fear was associated to lack of judgment and insight (P = 0.004). Conclusions Deficits in recognition of specific facial emotions may reflect severity of psychiatric symptoms. They may be related to specific clusters of psychotic symptoms, which need to be confirmed in further studies. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| 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".