Clinical analysis of sarcopenia prevalence and its influencing factors in patients with Parkinson’s disease
Bibliographic record
Abstract
Background Patients with Parkinson’s disease (PD) have a high risk of sarcopenia. Herein, we evaluated the prevalence of sarcopenia and factors associated with it in ambulatory patients with PD. Methods Ambulatory patients with PD up to Hoehn and Yahr stage III were included and evaluated based on age, sex, disease duration, levodopa equivalent daily dose, cognitive impairment, swallowing disturbance, history of falling, the Japanese version of the movement disorder society-sponsored revision of the unified PD rating scale (MDS-UPDRS) parts I–IV, quality of life (QoL), and blood test (total protein, albumin, and anemia). Cognitive impairment was assessed using the Japanese version of Mini-Mental State Examination and the Japanese version of the Montreal Cognitive Assessment, whereas swallowing disturbance was assessed using the Japanese version of the Swallowing Disturbance Questionnaire. QoL was assessed using the Parkinson’s Disease Questionnaire (PDQ-8). Sarcopenia was diagnosed based on handgrip strength, five-time chair stand test, and skeletal muscle mass. Results Overall, 97 patients with PD (55 males), with a mean age of 69.8 years and a mean disease duration of 7.3 years, were included. The prevalence of sarcopenia was 33.0%. There were significant differences between the sarcopenia and the non-sarcopenia groups in age, sex, swallowing disturbance, MDS-UPDRS part III total score, and the sub-items arising from a chair, postural stability, and global spontaneity of movement ( p < 0.05). There was no association between the presence of sarcopenia and the PDQ-8 total and sub-item scores. The factors that contributed most to sarcopenia were being female, cognitive impairment, and swallowing disturbance. Conclusion In clinical management, it is important to assess muscle strength and evaluate sarcopenia, particularly in patients with PD who have being female, cognitive impairment, or swallowing disturbance.
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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) | 0.001 | 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".