The Characteristics of Primary and Secondary Negative Symptoms in Patients with Schizophrenia, and Their Impact on Social Functioning
Bibliographic record
Abstract
Aim. To study the clinical characteristics of primary and secondary poles of negative disorders, determine the role of inflammation in their formation, and assess the impact of their manifestations on the social functioning of patients with schizophrenia. Design. Cross-sectional study. Materials and Methods. The study included 42 patients diagnosed with paranoid schizophrenia with a disease duration of more than 3 years. The median age of patients was 40 (29–47) years. Patients were at the stage of remission formation, and negative symptoms dominated their clinical presentation. Clinical-anamnestic, clinical-psychopathological, and psychometric examination methods were applied. Psychometric tools included the Positive and Negative Syndrome Scale, Brief Negative Symptom Scale, Calgary Depression Scale for Schizophrenia Patients, Extrapyramidal Symptoms Rating Scale, and Personal and Social Performance Scale. Based on complete blood count data, systemic inflammation indices were calculated: neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and the systemic immune-inflammation index. Results. Cluster analysis identified two clusters depending on the predominance of primary or secondary negative symptoms (SNS). Despite comparable severity of negative symptoms across the clusters, the group with predominant SNS (characterized by pronounced depressive and extrapyramidal symptoms) demonstrated significantly better social functioning (p = 0.002). No significant correlations were found between negative symptoms and the level of systemic inflammation. Multiple linear regression analysis revealed a statistically significant influence of the severity of negative and depressive symptoms, as well as disease duration, on the social and personal functioning of schizophrenia patients (F = 17.1, p < 0.001, R2 = 0.64, AIC = 248, d = 1.64). Conclusion. According to the obtained data, dominant negative symptoms in the clinical picture of schizophrenia patients can be conceptualized within a two-cluster model based on the predominance of primary or secondary components of negative disorders. In patients with dominant negative symptoms, the presence of depressive disturbances acts as a predictor of better social functioning. Keywords: paranoid schizophrenia, negative symptoms, secondary negative symptoms, social functioning, cluster analysis, neuroinflammation.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".