Evidence map›Paper›PMID 29850150›Full record

ArticleJournal of thoracic disease2018

The top 2,000 cited articles in critical care medicine: a bibliometric analysis.

Zhongheng Zhang, Sven Van Poucke, Hemant Goyal, Daniel D Rowley, Ming Zhong, Nan Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of thoracic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. AI in phishing detection: a bibliometric review.Frontiers in artificial intelligence · 2025
    Pooled it
  2. Pooled it
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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

6 authors.

Zhongheng ZhangDepartment of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Sven Van PouckeDepartment of Anesthesiology, Critical Care, Emergency Medicine and Pain Therapy, Genk, Belgium.
Hemant GoyalDepartment of Internal Medicine, Mercer University School of Medicine, Macon, GA, USA.
Daniel D RowleyPulmonary Diagnostics & Respiratory Therapy Services, University of Virginia Medical Center, Charlottesville, VA, USA.
Ming ZhongDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200000, China.
Nan LiuDuke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe bibliometric analysis has been performed on several topics in critical care medicine (CCM) focusing on top 100 cited articles, but the analysis on CCM literature as a whole is missing. The present study aimed to perform a complete bibliometric analysis in the field of CCM.

methodsAn electronic search of the Scopus database was performed on Feb 13, 2018. The search strategy involved core terms related to CCM. The top 2,000 most cited articles in the field of CCM were included in the analysis. Descriptive statistics on these top-cited articles, country distributions, and journals are reported. Individual author's productivity was assessed with the Lotka's law. Co-occurrence of keywords was visualized with the Fruchterman-Reingold layout. The Walktrap algorithm was employed for clustering analysis.

resultsA total of 2,000 documents were included in the analysis with median citations of 386 times [interquartile range (IQR): 308-562 times]. The most cited article was the original paper that described the Acute Physiology and Chronic Health Evaluation (APACHE) II score. The included articles were published in 411 journals. The median number of documents published in one journal was 1, and the mean number was 4.9, indicating a skewed distribution. The maximum number of publications was 217 in CCM. Author's productivity profile was significantly different from the Lotka's law (P=0.001), with n and C values of 2.8 and 0.52, respectively. Fruchterman-Reingold network plot showed that studies involving human subject were the most common literature type. Sepsis was a major research topic that co-occurred with keywords such as disease severity, nonhuman, risk assessment and practice guideline.

conclusionsThe study performed bibliometric analyses of 2,000 top-cited articles in CCM. The most cited article was the one which developed the APACHE II score. Author's productivity was significantly different from the Lotka's law.

Indexed as

Bibliometriccritical careFruchterman-Reingold layoutLotka’s lawsepsistop cited

Identifiers

PMID29850150
PMCPMC5949497

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.