Sensori- and psychomotor abnormalities, psychopathological symptoms and functionality in schizophrenia-spectrum disorders: a network analytic approach
Bibliographic record
Abstract
Sensori- and psychomotor abnormalities are an inherent part of schizophrenia-spectrum disorders (SSD) pathophysiology and linked to psychopathological symptoms as well as cognitive and global functioning. However, how these different symptom clusters simultaneously interact with each other is still unclear. Here, we examined 192 SSD patients (37.75 ± 12.15 years, 73 females). First, we investigated the cross-sectional prevalence and overlap of individual sensori- and psychomotor abnormalities. Second, we applied network analysis methods to simultaneously model the associations between Neurological Soft Signs (NSS), level of akathisia, parkinsonism symptoms, tardive dyskinesia (TD) and catatonia signs as well as cognition, psychopathology, global functioning and daily antipsychotic dose. The largest centralities were exhibited by NSS (0.90), catatonia signs (0.82) and global functioning (0.79). NSS showed strong partial correlations with cognition and parkinsonism symptoms (edge weight, ew = 0.409 and ew = 0.318, respectively). Catatonia signs showed strong connections with global functioning (ew = 0.333). In contrast, TD, akathisia and daily antipsychotic dose were weakly connected with other variables (e.g., largest ew=0.176 between TD and akathisia). In conclusion, NSS and cognition, parkinsonism symptoms and NSS as well as catatonia signs and global functioning seem to be preferentially connected in SSD. The daily medication had little influence on sensori- and psychomotor abnormalities, indicating that they are features of core SSD pathophysiology. Future studies should incorporate these relationships to enhance the understanding of SSD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".