Evidence map›Paper›PMID 36159564›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

Li Chen, Tao Yin, Zhaoxuan He, Yuan Chen, Ruirui Sun, Jin Lu, Peihong Ma, Fang Zeng

Abstract read
In one paragraph

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Psychophysical responses to needling depth using ultrasound imaging.BMC complementary medicine and therapies · 2025
    Article
  6. Article
  7. 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

8 authors.

Li ChenAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-5807-1835
Tao YinAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-9429-7153
Zhaoxuan HeAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0001-6984-9318
Yuan ChenInternational Education School, Chengdu University of Traditional Chinese Medicine Chengdu, Chengdu 610075, China.ORCID https://orcid.org/0000-0001-9426-4442
Ruirui SunAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-6887-5968
Jin LuAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-2471-8143
Peihong MaSchool of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China.ORCID https://orcid.org/0000-0002-1621-6194
Fang ZengAcupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0002-7701-2574

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial Methods: This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Results: The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an Conclusion: Based on the SVM algorithms and

Identifiers

PMID36159564
PMCPMC9492368

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.