Немоторные симптомы болезни Паркинсона, их структура и влияние на качество жизни пациентов
Bibliographic record
Abstract
Parkinson's disease (PD) includes motor and non-motor types of symptoms that have a considerable influence on quality of life and restrict everyday and social activity. The aim of the research was to study the availability, the frequency, the structure and the evidence of non-motor symptoms in different stages of PD to appreciate their influence on quality of patients' life and correlation between duration of PD and basic motor characteristics of disease. The materials and the methods: we examined 54 patients with the diagnosis of Parkinson's disease having used the Unified Parkinson's Disease Rating Scale (UPDRS), the Montreal Cognitive Assessment (МоСА), the Beck Depression Inventory, the Non-Motor Symptoms Scale (NMSS) and the Non-motor symptoms Questionnaire (PD NMS). The results: non-motor symptoms are the obligatory signs in the structure of PD. We discovered the significant dependence of evidence of non-motor symptoms on the stage and the points of UPDRS. We can note the dependence of quality of life on the case, the stage of the disease, the evidence of depression and the patients' age. This research showed that quality of patients' life is more dependent on evidence of motor impairments than on non-motor types of symptoms. Conclusion: non-motor symptoms are the obligatory signs of Parkinson's disease and have a considerable influence on quality of patients' life.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".