Association of Cortical Thickness and Neurological Soft Signs in Patients with Chronic Schizophrenia and Healthy Controls
Bibliographic record
Abstract
BACKGROUND: Neurological soft signs (NSS), i.e. subtle neurological abnormalities, have been frequently found in schizophrenia. Neuroimaging studies in schizophrenia have shown abnormal cortical thickness changes across the cortical mantle. However, few studies have examined relationships between NSS and cortical thickness abnormalities in schizophrenia. METHOD: A sample of 18 patients with chronic schizophrenia and 20 age-matched healthy controls were included. Cortical thickness was assessed on high-resolution 3-tesla magnetic resonance imaging by using FreeSurfer software and NSS were rated on the Heidelberg Scale. RESULTS: Significant negative correlations between NSS and cortical thickness were found in the prefrontal, inferior temporal, superior parietal, postcentral, and supramarginal cortices in the schizophrenia patients. In the controls, however, this negative correlation was found in the anterior cingulate, pericalcarine and superior/middle temporal regions. CONCLUSION: Our results not only confirmed the association between NSS and cortical thickness in chronic schizophrenia but also indicated that patients and controls have different anatomical substrates of NSS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".