The Effectiveness and Safety Analysis of Duloxetine in Treating Comorbid Depression in Parkinson's Disease: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder characterized by both motor and non-motor symptoms, including depression, which significantly impacts the quality of life of affected individuals. This study aims to investigate the real-world effectiveness and safety of duloxetine in treating comorbid depression in patients with Parkinson's disease and to compare its outcomes with traditional treatment approaches. METHODS: This study included adult patients diagnosed with Parkinson's disease combined with depression from December 2020 to December 2023. Based on the use of duloxetine, the cohort was divided into a traditional treatment group and a duloxetine group (traditional treatment combined with duloxetine). Patients with incomplete medical records, concurrent antidepressant therapy, or major psychiatric or neurological disorders were excluded. Retrospective data, including demographic information, treatment adherence, and various assessment scores, were collected from medical records by trained research staff. RESULTS: In total, 106 patients were analyzed, with 50 patients receiving traditional treatment and 56 patients receiving duloxetine. The duloxetine group exhibited significantly lower scores than the traditional treatment group in the Unified PD Rating Scale (p = 0.015), Hamilton Depression Rating Scale (p = 0.013), Beck Depression Inventory (p = 0.031), Parkinson's disease Questionnaire-39 (p = 0.006), and Clinical Global Impression-Improvement (p < 0.001) scores. In motor function assessment, the duloxetine group demonstrated improvements in kinetic tremor scores (p = 0.017), gait speed (p < 0.001), Timed Up and Go Test performance (p < 0.001), dyskinesia severity (p = 0.017), and rigidity (p = 0.019) compared to the traditional treatment group. Additionally, the duloxetine group exhibited better cognitive function across various assessments, including the Symbol Digit Modalities Test (p = 0.024), Stroop Color-Word Test (p = 0.048), and Montreal Cognitive Assessment (p = 0.024). CONCLUSION: Duloxetine is associated with superior efficacy in improving motor and non-motor symptoms, overall clinical status, and cognitive function. These findings support the potential utility of duloxetine as a comprehensive treatment option for comorbid depression in Parkinson's disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".