Prevalence of nocturia in Parkinson’s disease patients from various ethnicities
Bibliographic record
Abstract
OBJECTIVES: One of the most common non-motor symptoms in Parkinson's disease (PD) is nocturia. This paper seeks to address the prevalence of nocturia in PD and correlate it to various factors such as gender, Hoehn and Yahr (H&Y) stage, age, and ethnicities. METHODS: In particular, 332 PD patients were seen in a community movement disorders clinic and their charts were analyzed from 2005 to 2010. Within this population, more than one-third (34.9%) patients were diagnosed with nocturia. RESULTS: Age, gender, and PD stage were significant predictors of nocturia in PD. With every one-year increase in age, the odds of developing nocturia in PD increases by 3.1% while an increase in H&Y stage increases the odds of nocturia in PD by 1.645 times. Also, males had greater odds of experiencing nocturia in PD. Ethnicities alone were of no significant importance. However, after performing interaction analyses, Asian and Indian males, especially, were at significantly greater risk than other ethnicities. DISCUSSION: Future research is indeed required to understand why certain ethnicities are especially at risk. Clinicians must also be aware of the epidemiology of nocturia in PD to prevent and treat this debilitating symptom.
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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) | 0.002 | 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".