Evidences of neurodegenerative processes in patients with late-onset schizophrenia and cognitive impairment
Bibliographic record
Abstract
Introduction The large proportion of patients with late-onset schizophrenia (LoS) has cognitive impairment. We hypothesized that this group of patients could have more risk factors associated with neurodegeneration. Objectives The aimed to compare various clinical and risk factors in LoS patients with low and relatively preserved cognitive status. Methods 28 LoS patients (ICD-11) with duration of disease less than 10 years from a cohort of patients with late onset psychosis underwent clinical assessment (PANSS, HDRS-17), cognitive examination (MMSE, MoCA, FAB, verbal and symbolic memory, trail making test (part A, B)), structured interviewing on risk factors and CT. Hierarchical cluster analysis of cognitive test results was applied. Nonparametric statistic was used to compare control group (24 subjects with signs of psychosis or depression, age 58,1±10,8, 50% females) and patient`s groups. Results Patients were divided on two clusters: Cluster 1 with lower cognitive functions (n=20, age 62,2, 94% of females) and Cluster 2 with preserved cognitive functions (n=8, age 56,8, 100% of females). Patients of Cluster 1 were older, had more negative symptoms, higher atrophy scores, higher rate of leukoaraiosis on CT and more history of mild brain injury than patients of Cluster 2 and controls. There was no group differences in age of manifestation, other PANSS scores, rates of social phobia and number of habitual anxiety reactions between clinical groups. Conclusions LoS patients with cognitive deficiency had more factors associated with neurodegeneration, in particular history of mild brain injury. Disclosure No significant relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".