Impact of Rivastigmine on Cognitive Dysfunction and Falling in Parkinson's Disease Patients
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to observe the incidence of falls in Parkinson's disease (PD) patients with different cognitive levels and to investigate the effect of the cholinesterase inhibitor Rivastigmine on cognitive dysfunction and falling in PD patients. SUBJECTS AND METHODS: Data from 176 PD patients participating in the collaborative PD study between June 2010 and June 2014 were collected; the Chinese edition of the Montreal Cognitive Assessment (MoCA) score was used to evaluate the cognitive function of patients, and falls were recorded. PD patients with cognitive dysfunction were randomly administered either a placebo or Rivastigmine. The cognitive function changes and difference in fall incidence were compared between the 2 groups. RESULTS: The average number of falls per person in PD patients without cognitive impairment dysfunction was significantly lower than that in patients in the PD mild cognitive impairment (PD-MCI) group and that in the PD dementia (PDD) group (p < 0.01, p < 0.001, respectively), and the incidence of falls was significantly lower than that in patients in the PD-MCI and PDD groups (p < 0.01, p < 0.01, respectively). Compared to the PD-MCI group, the incidence of falls of patients in the PDD group (OR 2.45, 95% CI 0.97-6.20, p < 0.01) and the number of falls per person were significantly increased (p < 0.01). After taking the placebo or Rivastigmine for 12 months, the MoCA scores of patients in the Rivastigmine treatment group were significantly higher than those of the control group (p = 0.002). The number of falls per person and the incidence of falls of patients in Rivastigmine treatment group were significantly lower than those in the placebo group (p < 0.01). CONCLUSION: This study suggests that the degree of cognitive impairment is closely associated with the incidence of falls, and the cholinesterase inhibitor Rivastigmine can delay the deterioration of cognitive function and lower the incidence of falls in PD patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".