Self-assessment of social cognition in a sample of Lebanese in-patients with schizophrenia
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to evaluate social cognitive complaints in a sample of chronic in-patients with schizophrenia and compare it to healthy controls. The secondary objective was to explore factors related to social cognitive complaints in these patients, such as neurocognition, clinical symptoms, depression, and insight. METHODS: A cross-sectional study conducted between July 2019 and March 2020 at the Psychiatric Hospital of the Cross (HPC)-Lebanon enrolled 120 chronic in-patients diagnosed with schizophrenia and schizoaffective disorders and 60 healthy controls. The Self-Assessment of Social Cognition Impairments (ACSo) scale was used to assess social cognitive complaints. RESULTS: A significant difference was found between schizophrenia patients and healthy controls in all social cognitive complaints: theory of mind complaint, attributional biases complaint, emotional processes complaint, and social perception and knowledge complaint (p < 0.001 for all). All objective cognitive disorders were significantly associated with social cognitive complaints except for attention and speed of information processing. Higher verbal memory and verbal fluency were significantly associated with lower emotional processes complaint scores. The results of the multivariate analysis showed that a higher cognition (Beta = -0.08, p = 0.001) was significantly associated with a lower social cognitive complaint, contrary a higher depression (Beta = 0.38, p = 0.04) was significantly associated with a higher social cognitive complaint, in particular attributional biases complaints. CONCLUSION: This study showed that patients with schizophrenia have complaints about their social cognition. It could also demonstrate that subjective social cognitive complaints are correlated with depressive symptoms and objective cognitive deficits among these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".