Low serum uric acid levels and levodopa-induced dyskinesia in Parkinson's disease
Bibliographic record
Abstract
Abstract Background Levodopa is the most used and effective medication for motor symptoms of Parkinson disease (PD), its long-term use is associated with the appearance of levodopa-induced dyskinesia (LID). Uric acid (UA) is believed to play an important neuroprotective role in PD. Objective To investigate if serum UA levels are related with the presence of LIDs in PD patients. Also, we investigated the associations among UA levels and clinical features of PD. Methods We enrolled 81 PD patients (dyskinesia = 48; no dyskinesia = 33) in the present study. A blood sample was collected to evaluate serum UA levels, clinical evaluation included the following instruments: Montreal Cognitive Assessment (MoCA), Beck Depression Inventory II (BDI-II), MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (HY), and the sub-item 4.1 of MDS-UPDRS IV (score ≥ 1). Additional relevant clinical information was obtained by a clinical questionnaire. Results Serum UA levels were lower in the dyskinesia group when compared with the no dyskinesia group. The same result was found in the UA levels of both men and women. The multivariate analysis showed lower uric acid levels were significantly associated with having dyskinesia (odds ratio [OR] = 0.424; 95% confidence interval [CI]: 0.221–0.746; p = 0.005). Additional analysis verified that serum UA levels are inversely correlated with depressive symptoms, disease duration, MDS-UPDRS IV and time spent with dyskinesia. A positive correlation was found with age at onset of PD symptoms. Conclusions The present study provides a possible role of serum UA levels in LID present 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".