ArticleEvidence-based complementary and alternative medicine : eCAM2022
Efficacy of Traditional Herbal Medicine Treatment Based on Pattern Identification for Idiopathic Parkinson's Disease: A Protocol for Systematic Review and Meta-Analysis.
Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Efficacy of herbal medicine treatment based on syndrome differentiation for Parkinson's disease: A systematic review and meta-analysis of randomized placebo-controlled clinical trials.Frontiers in pharmacology · 2023Pooled it
- The Potentiality of Natural Products and Herbal Medicine as Novel Medications for Parkinson's Disease: A Promising Therapeutic Approach.International journal of molecular sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD), the second most common progressive neurodegenerative disease, is characterized by various clinical symptoms and reduced quality of life. The standard dopaminergic therapy for PD has limitations such as drug wear-off, drug-related side effects, and drug-resistant PD symptoms. Traditional oriental medicine, which is a personalized approach based on pattern identification (PI), has been reported to relieve symptoms, halt disease progression, and improve the quality of life in patients with PD. This comprehensive systematic review will be conducted to gather clinical studies related to complementary traditional herbal therapies based on PI for idiopathic PD and assess its effectiveness. Clinical studies, including randomized controlled trials in English, Korean, and Chinese databases related to the efficacy of herbal medicine based on PI for PD will be searched in computer retrieval. In addition, the subdivided PI for each clinical manifestation of PD will be investigated. Two researchers will independently screen and select studies, extract data, and assess bias risk. The risk of bias will be evaluated using the Cochrane risk-of-bias assessment tool. After screening the studies, a meta-analysis will be performed. The primary outcome will be the unified Parkinson's disease rating scale to measure clinical symptom reduction. Secondary outcomes will consist of other validated scales to evaluate the improvement of PD, including improvement of clinical symptoms and quality of life. The quality of evidence will be evaluated through the Grading of Recommendations, Assessment, Development, and Evaluation pro. Complementary traditional medicine is a personalized medicine that classifies individual states based on PI. We expect that the results of this review will provide evidence for the efficacy of traditional herbal medicine based on PI for the treatment of PD. This protocol has been registered in the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) 2021 (registration number INPLASY2021100020).
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.