Evidence map›Paper›PMID 35449821›Full record

ReviewEvidence-based complementary and alternative medicine : eCAM2022

Publication Trends in Rehabilitative Effects of Acupuncture: A Visual Analysis of the Literature.

Yanmei Zhong, Jihui Cao, Haizhen Lu, Zonghai Huang, Lu Liu, Chuanbiao Wen, Ji Chen

Open access · hybridAbstract readReview
In one paragraph

Review 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

7 authors at 1 institution in 1 country.

Yanmei ZhongSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-5089-2348
Jihui CaoDepartment of Orthopedics, Renshou County Hospital of Traditional Chinese Medicine, Chongqing 620500, China.
Haizhen LuBasic Medical School of Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China.ORCID https://orcid.org/0000-0002-1893-3090
Zonghai HuangSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0003-3891-8354
Lu LiuSchool of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.
Chuanbiao WenSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0003-2279-932X
Ji ChenSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-4294-0370
Chengdu University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To conduct a comprehensive analysis of scientific outputs in 2011-2021 regarding the rehabilitative effects of acupuncture on diseases. Methods: The study was conducted in the form of knowledge graph and data visualization, with data being drawn from the Web of Science Core Collection database. Results: Articles and reviews were the dominant types; China, Guangzhou University of Chinese Medicine and Conclusions: This article provided a relatively panoramic picture of the scientific outputs in acupuncture for disease rehabilitation, which may help readers embrace the heated topic and grasp the recent research focus on this field.

Identifiers

PMID35449821
PMCPMC9017514
OpenAlexW4223597604

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.