Impact of Cognitive Dysfunction on Drooling in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is commonly characterized by its motor symptoms such as resting tremor, rigidity, bradykinesia and postural instability; however, some of the most debilitating symptoms of this disease are non-motor ones such as dementia and sialorrhea (drooling). Drooling is caused by impaired swallowing and it can have a significant impact on the quality of life. However, it is still unclear whether cognitive dysfunction could exacerbate drooling. We wanted to examine if any relationship existed between drooling and dementia in PD patients. Identifying the correlation will aid physicians to screen and initiate early management of drooling in the course of PD. This can possibly lead to improvements in the quality of life in these patients. METHODS: In this retrospective study, we investigated the prevalence of drooling in 314 PD patients and further compared the difference in the prevalence of drooling in patients with dementia and without dementia. In addition, we studied the impact of gender on drooling in this patient population. RESULTS: Our results show that a significant correlation exists between drooling and dementia in our sample of PD patients. Furthermore, in males, the correlation between the prevalence of drooling and dementia was found to be clinically significant as compared to the female population. CONCLUSION: Our findings suggest that drooling is a major concern in the course of PD and should therefore be addressed early and more aggressively in patients with dementia.
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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 | 0.004 |
| 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 | 0.001 | 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".