Associations between theory of mind and clinical symptoms in recent onset schizophrenia spectrum disorders
Bibliographic record
Abstract
Introduction People with schizophrenia often present with Theory of mind (ToM) deficits, and the link between these deficits and clinical symptoms remains to be refined, for instance through the use of more recent assessment methods. The objective of this study was to examine the associations between a psychometrically sound ToM task and the clinical symptoms of schizophrenia as measured with the five dimensions of the Positive and Negative Syndrome Scale (PANSS) namely positive, negative, cognitive/disorganization, depression/anxiety and excitability/hostility, while controlling for non-social cognitive abilities. Methods Seventy participants with recent-onset schizophrenia spectrum disorders (SSD) were assessed for ToM using the Combined stories task (COST) and for clinical symptoms using the PANSS. Results The results revealed significant correlations between ToM and the positive (r= −0.292,p= 0.015) and cognitive/disorganization (r= −0.480,p< 0.001) dimensions when controlling for non-social cognitive abilities. In contrast, the negative symptoms dimension was only significantly correlated with ToM when non-social cognitive abilities were not controlled for (r= −0.278,p= 0.020). Discussion Very few prior studies used the five-dimensions of the PANSS to examine the link with ToM and this study is the first to rely on the COST, which includes a non-social control condition. This study highlights the importance of taking non-social cognitive abilities into account when considering the relationship between ToM and 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".