Evidence map›Paper›PMID 37363407›Full record

ReviewWorld journal of acupuncture-moxibustion2023

Acupoint stimulation for long COVID: A promising intervention:.

Bo-Wen Feng, Pei-Jing Rong

Open access · greenAbstract readReview
In one paragraph

Review in World journal of acupuncture-moxibustion, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Bo-Wen FengInstitute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China (,100700,).
Pei-Jing RongInstitute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China (,100700,).
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

"Long COVID" is a sustained symptom following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). According to recent statistics, at least 65 million people have long COVID, which poses a long-term threat to human health. The pathogenic mechanisms of coronavirus disease 2019 (COVID-19) are complex and affect multiple organs and systems. Common symptoms include palpitations, breathing difficulties, attention and memory deficits, fatigue, anxiety, and depression. It is difficult to achieve satisfactory treatment effect with a single intervention. Currently, treatment strategies for long COVID are still in the exploratory stage, and there is an urgent need to find appropriate and effective methods for long COVID treatment. Traditional Chinese medicine is effective in treating the various phases of COVID-19. Previous studies have shown that acupoint stimulation therapy is effective in improving palpitations, dyspnea, cognitive impairment, anxiety, depression, and other symptoms in patients. According to previous studies, acupoint stimulation may improve various symptoms related to long COVID. This paper discusses the potential application value of acupoint stimulation in the treatment of long COVID-related symptoms, based on the common sequelae of various systems involved in long COVID, and the effect of acupoint stimulation in the treatment of similar symptoms and diseases in recent years.

Indexed as

Acupoint stimulationLong COVIDnovel coronavirus pneumoniaSARS-CoV-2Sequelae

Identifiers

PMID37363407
PMCPMC10232723
OpenAlexW4379012113

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.