Возрастные особенности нейрокогнитивного дефицита у больных шизофренией и расстройствами шизофренического спектра на начальных этапах заболевания
Bibliographic record
Abstract
The goal of this investigation was to study the characteristics of neurocognitive deficit in patients with schizophrenia and schizophrenia spectrum disorders during the first episode of disease at the stage of improvement, in different age groups. Material and methods: psychiatric patients diagnosed as schizophrenia or schizophrenia spectrum disorder, aged at least 18 years and with the history of disease no more than 5 years (with maximum three previous psychotic episodes). Patients were investigated with the Brief Assessment of Cognition in Schizophrenia (BACS), Positive and Negative Syndromes Scale (PANSS) and Calgary Depression Rating Scale (CDRS). The data obtained demonstrated presence of neurocognitive deficit in schizophrenic patients in comparison with normative parameters as well as varying severity of impairments. Statistically significant differences with the norm most frequently were found in patients younger than 40 years of age. Within the patient sample better results showed patients aged 30–39 years, though in many cases patients aged 40 years and older completed the tasks better than patients under 30 years of age. In cases when the results of the eldest age group were worse, the differences happened to be minimal or significantly less obvious than in control group. So, we suggest that cognitive impairments seem to be more prominent in cases of early onset of disease while in later manifestation of disease cognitive deficit is less obvious.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".