Evidence map›Paper›PMID 36684199›Full record

ArticleFrontiers in surgery2022

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning.

Guoxin Fan, Yufeng Li, Sheng Yang, Jiaqi Qin, Longfei Huang, Huaqing Liu, Shisheng He, Xiang Liao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 29% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. 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 at 7 institutions in 1 country.

Guoxin FanDepartment of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China.
Yufeng LiDepartment of Sports Medicine, The Eighth Affiliated Hospital Sun Yat-sen University, Shenzhen, China.
Sheng YangSpinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China.
Jiaqi QinArtificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
Longfei HuangDepartment of Orthopedics, Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, China.
Huaqing LiuArtificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
Shisheng HeSpinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China.
Xiang LiaoDepartment of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China.
Pearl River Hydraulic Research Institute · CNAnyang Hospital of Traditional Chinese Medicine · CNHuazhong University of Science and Technology · CNShanghai Tenth People's Hospital · CNSun Yat-sen University · CNTongji University · CNUnion Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining. Methods: The data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts. Results: Up to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years. Conclusions: The study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

Indexed as

bibliometricslatent dirichlet allocationlumbar spondylolisthesismachine learningtext mining

Identifiers

PMID36684199
PMCPMC9852633
OpenAlexW4313646299

What Socratic 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 Socratic graph.