Evidence map›Paper›PMID 38850480›Full record

ArticleChinese journal of integrative medicine2024

Topic Modeling Analysis of Chinese Medicine Literature on Gastroesophageal Reflux Disease: Insights into Potential Treatment.

Jia-Nan Qian, Yan-Lan Kang, You-Cheng He, Hong-Yi Hu

Abstract read
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In one paragraph

Article in Chinese journal of integrative medicine, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. What drives EBSA across contexts? evidence from China using BERTopic.Frontiers in child and adolescent psychiatry · 2026
    Article
  3. 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

4 authors.

Jia-Nan QianDepartment of Gastroenterology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
Yan-Lan KangInstitute of AI and Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China.
You-Cheng HeClinical Research Center, Longhua Hospital, Shanghai, 200032, China.
Hong-Yi HuDepartment of Gastroenterology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China. hongyihu2003@shutcm.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze Chinese medicine (CM) prescriptions for gastroesophageal reflux disease (GERD), we model topics on GERD-related classical CM literature, providing insights into the potential treatment.

methodsClinical guidelines were used to identify symptom terms for GERD, and CM literature from the database "Imedbooks" was retrieved for related prescriptions and their corresponding sources, indications, and other information. BERTopic was applied to identify the main topics and visualize the data.

resultsA total of 36,207 entries are queried and 1,938 valid entries were acquired after manually filtering. Eight topics were identified by BERTopic, including digestion function abate, stomach flu, respiratory-related symptoms, gastric dysfunction, regurgitation and gastrointestinal dysfunction in pediatric patients, vomiting, stroke and alcohol accumulation are associated with the risk of GERD, vomiting and its causes, regurgitation, epigastric pain, and symptoms of heartburn.

conclusionsTopic modeling provides an unbiased analysis of classical CM literature on GERD in a time-efficient and scale-efficient manner. Based on this analysis, we present a range of treatment options for relieving symptoms, including herbal remedies and non-pharmacological interventions such as acupuncture and dietary therapy.

Indexed as

Gastroesophageal RefluxMedicine, Chinese TraditionalDrugs, Chinese HerbalHumansModels, TheoreticalDrugs, Chinese HerbalBERTopicChinese medicinegastroesophageal reflux diseaseprescriptiontopic modeling

Identifiers

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Registered trials

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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.