Assessment of Clinical and Demographic Factors Influencing the Severity of Levodopa-Induced Dyskinesia
Bibliographic record
Abstract
ABSTRACT Background: Levodopa-induced dyskinesia (LID) is a disabling symptom of Parkinson’s disease (PD). There have been prior attempts to find risk factors contributing to this symptom, but risk factors for the severity of LID have not been comprehensively studied. We aimed to evaluate factors that correlate with LID severity in patients with PD based on the Unified Dyskinesia Rating Scale (UDysRS). Methods: A cross-sectional study was designed on 52 idiopathic PD patients who were referred for LID between 2023 and 2024. Their demographic and clinical records were studied. Furthermore, cognitive decline (MoCA), PD severity (Hoehn and Yahr) and the severity of dyskinesia (UDysRS) were examined. The association between factors and LID severity was evaluated by carrying out univariate regression and multivariate regression backward elimination analysis. Results: The mean age of patients with LID was 59.9 ± 11.4 years. Results of univariate regression analysis indicated that male sex ( β = −0.24, P = 0.04), BMI ( β = −0.3, P = 0.005), H&Y ( β = 0.4, P = 0.002), diabetes mellitus ( β = 0.3, P = 0.018) and levodopa dosage per kilogram ( β = 0.37, P = 0.01) were significant factors involved in the severity of dyskinesia. The univariate regression model results showed that lack of constipation ( P = 0.04), hyperlipidemia ( P = 0.04) and total daily levodopa dosage per kilogram ( P = 0.01) were associated with the severity of end-dose dystonia. Conclusion: This study revealed that female sex, more advanced PD, diabetes mellitus, daily levodopa dosage per kilogram body weight and BMI are associated with the severity of LID. Also, it suggests that hyperlipidemia and lack of constipation are associated with the severity of end-dose dystonia.
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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 | 0.000 |
| 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) | 0.002 | 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".