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Record W4403174397 · doi:10.62641/aep.v52i5.1634

The Effectiveness and Safety Analysis of Duloxetine in Treating Comorbid Depression in Parkinson's Disease: A Retrospective Study

2024· article· en· W4403174397 on OpenAlexaboutno aff
Zhuoqun Wang, Jing Tian, Weixin Dai, Na Zhang, Jianglin Wang, Zhan-Yu Li

Bibliographic record

VenueActas Españolas de Psiquiatría · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDuloxetineMedicineRating scaleDepression (economics)Beck Depression InventoryDyskinesiaParkinson's diseaseClinical Global ImpressionInternal medicineRetrospective cohort studyMovement disordersPhysical therapyPsychiatryDiseasePsychologyAnxiety

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.288
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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