Clinical and Epidemiological Aspects of Parkinson's Disease in the South of Western Siberia
Bibliographic record
Abstract
Тhe article is devoted to one of the most common neurodegenerative diseases in the world - Parkinson's disease (PD), the prevalence of which in Russia reaches 140-150 people per 100 thousand people. The clinical and anamnestic profile of a patient with PD is presented, the prevalence of motor and non-motor symptoms is reflected, and a comparative characteristic of the neurological deficit in the Siberian population of patients with other cohorts of patients with Parkinson's disease is presented. We studied 140 patients with Parkinson's disease. A comprehensive assessment of neurological status was performed using the “Unified Parkinson's Disease Rating Scale (UPDRS). In addition, we used the Beck Depression and MoCA scale - test. The cohort of the study was dominated by overweight patients with a higher level of education, with concomitant arterial hypertension, coronary heart disease and dyslipidemia. The prevalence of motor and most non-motor symptoms depends on the stage of the disease. The predominant form of the disease was a mixed form, which was also noted in research cohorts in Canada and the UK. The Siberian cohort tends to be more prevalent in hyposmia, daytime sleepiness, orthostatic hypotension, and depressive and REM disorders. Our data show the importance of a comprehensive assessment of both motor and non-motor neurological deficits, as well as the analysis of comorbid disorders and risk factors for the occurrence and progression of Parkinson's disease. They also show the prevalence of certain motor and non-motor symptoms in the Siberian cohort of patients with Parkinson's disease.
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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 | 0.001 | 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) | 0.002 | 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".