Clinical symptomatology and theory of mind in schizophrenia: Which relationship?
Bibliographic record
Abstract
Introduction Theory of mind (ToM) has repeatedly been shown to be compromised in many patients with schizophrenia (SCZ). It now seems to be quite well-established that patients with profound negative or disorganized symptoms perform poorly on ToM tasks. By contrast, findings in patients with predominant positive symptoms are much more ambiguous. Objectives To investigate the relationship between ToM deficits and different symptoms dimensions in SCZ. Methods Fifty-eight outpatients with stable SCZ completed the intention-inferencing task (IIT), in which the ability to infer a character's intentions from 28 short comic strip stories is assessed. 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 The number of correct answers in the IIT negatively correlated with both the positive (P = 0.015) and negative (P < 0.0001) scales of the PANSS. ToM deficits were correlated with the conceptual disorganization, hallucinations and the suspiciousness/persecution items. The patients who had more false answers in the IIT also had significantly higher scores at the positive (P = 0.005), negative (P < 0.0001) and general (P < 0.0001) scales of the PANSS. Worse IIT performance correlated with a higher severity index in the CGI. No correlations were found between IIT scores and CDSS scores. Conclusions Our results confirm the relationship between ToM deficits and negative symtomps and suggest that ToM may also be correlated to specific positive symptoms. 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".