ArticleMedicine2020
Traditional Chinese medicine for myasthenia gravis: Study protocol for a network meta-analysis.
Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed, 6 citations in OpenAlex.
- Summary of the best evidence for the prevention and management of myasthenic crisis after thymectomy.Gland surgery · 2024Review
- Myasthenia gravis complicated with pulmonary infection byFrontiers in medicine · 2024Article
- Myasthenia Gravis: Novel Findings and Perspectives on Traditional to Regenerative Therapeutic Interventions.Aging and disease · 2023Article
- Systems Pharmacology and Molecular Docking Reveals the Mechanisms of Nux Vomica for the Prevention of Myasthenia Gravis.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Study on the Potential Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2022Article
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMyasthenia gravis (MG) is a disease that is difficult to cure, mainly manifested in the affected skeletal muscle weakness and morbid fatigue, which seriously affects the patients' daily life and work. A large number of randomized controlled trial have shown that Traditional Chinese medicine (TCM) has a good effect in treating MG. However, due to the variety of TCM treatment methods, its relative effectiveness and safety have not been verified. Therefore, this study will use a network meta-analysis method to verify the effectiveness and safety of different types of TCM in the treatment of MG.
methodsWe will search the following databases from inception to June 2020: the China National Knowledge Infrastructure, Wanfang Database, Chinese Science and Technology Periodical Database, Chinese Biomedical Literature Database, Pubmed, Embase, Web of Science, and the Cochrane library. Collect all randomized controlled trial of TCM for the treatment of MG, The 2 authors will independently select studies and extract data based on pre-designed inclusion and exclusion criteria. Methodological quality assessment and risk of bias will be assessed using Cochrane bias risk tool. All data analysis will be conducted using Revman5.3, WinBUGS 1.4.3, and Stata14.2 software.
resultsThis study will directly and indirectly compare the different outcome indicators of various studies, and rank the effectiveness of different TCM methods. The main outcome indicators include effectiveness, remission rate (no drug symptoms), relapse rate, clinical absolute score, and relative score. Secondary outcome indicators: including any related adverse reactions, the concentration of acetylcholine receptor antibody in serum.
conclusionThe conclusion of this systematic review will provide credible Evidence-based for the relative advantages of different TCM treatment methods for MG.
Indexed as
Identifiers
What OpenQuestion holds
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.