Evidence map›Paper›PMID 37805745›Full record

ReviewZhonghua shao shang yu chuang mian xiu fu za zhi2023

[Research advances on severe burn infection and cytokine storm].

Z Y Chen, S Q Hu, D W Liu, H Y Zhang, G H Guo, Y G Mao

Open access · greenAbstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhonghua shao shang yu chuang mian xiu fu za zhi, 2023. 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
0.6field-weighted citation impact, top 35% 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, 1 citations in OpenAlex.

  1. Article
  2. Cytokine Storm in a Massively Burned Pediatric Patient.Journal of burn care & research : official publication of the American Burn Association · 2025
    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

6 authors at 2 institutions in 1 country.

Z Y ChenMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
S Q HuMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
D W LiuMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
H Y ZhangMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
G H GuoMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
Y G MaoMedical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
First Affiliated Hospital of Nanchang University · CNNanchang University · CN

Funding

Regional Science Fund Project of National Natural Science Foundation of China 81460293
6 · The paper itself

Abstract

Mortality due to severe burns has always been at a high level. A large number of studies have shown that the rapid onset of infectious symptoms and rapid progression of severely burned patients are closely related to the occurrence of cytokine storm. However, in clinical practice, cytokine storm monitoring, early warning, and symptomatic treatment are still in exploratory stage. This article reviews the cytokine storm and its related cytokines, the mechanism, early warning, and treatment of cytokine storm induced by burn infection, aiming to provide clinical references for reducing infection and mortality in severely burned patients.

Indexed as

BurnsCytokine Release SyndromeCytokinesHumansCytokines

Identifiers

PMID37805745
PMCPMC11630233
OpenAlexW4387432911

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.