A Study of Neurological Soft Signs and Cognition in Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Neurological soft signs (NSS) are delicate neurological abnormalities that comprise deficits in motor coordination, problems with the sequencing of complex motor acts, and sensory integration difficulties. These are nonspecific with no specific localization in the brain. NSS are found in many patients with Schizophrenia. Cognitive dysfunctions are also present in more than two-thirds of patients with Schizophrenia. This study aims at assessing the NSS and its association with cognitive impairment in patients with Schizophrenia. METHODS: A total of 100 Schizophrenia patients were included in the study. The Heidelberg scale was used for assessing the NSS. The Montreal Cognitive Assessment Scale (MoCA) for cognitive impairment, the Positive and Negative Syndrome Scale (PANSS) for Schizophrenia, and the Brief Psychiatric Rating Scale (BPRS) were used to assess the severity. Statistical analysis was performed by Pearson's Chi-square test, Kruskal-Wallis test, Wilcoxon rank tests and Spearman rank correlation along with mean and standard deviation. RESULTS: NSS were present in 68% (N=68) of the patients with motor coordination being maximally affected. Cognitive impairment was found in 73% (N=73) of patients with a MoCA score <26. Patients with predominant negative symptoms had higher NSS scores and lower MoCA scores. A "statistically significant" correlation was observed between cognitive impairment and NSS. Most patients with NSS and impaired cognition were in the "markedly ill" category of BPRS. CONCLUSION: A significant association was observed between cognitive deficits, negative symptoms, and NSS in Schizophrenia. NSS and cognitive dysfunctions are integral parts of Schizophrenia symptom domains and need to be assessed as the negative symptoms and severity of illness are associated with NSS, especially problems with motor coordination and cognitive dysfunctions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".