Evidence map›Paper›PMID 39497848›Full record

ArticleFrontiers in medicine2024

Rapid and precise identification of cervicothoracic necrotizing fasciitis caused by

Manna Zhao, Xuejun Leng, Jie Xu, Juanjuan Cui, Shuo Li, Weifeng Zhao

Abstract readCase Reports
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Manna ZhaoDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China.
Xuejun LengInfectious Diseases Department, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China.
Jie XuCenter of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, China.
Juanjuan CuiDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China.
Shuo LiKey Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China.
Weifeng ZhaoDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cervicothoracic necrotizing fasciitis (CNF) is one form of necrotizing soft-tissue infections, which could lead to patient demise during short course. Therefore, early recognition and immediate treatment contribute to promising prognosis of patients. Case presentation: A 58-year-old diabetic patient presented with a sore throat and progressive irritation of the neck and chest for 4 days. The initial diagnosis was considered to be soft-tissue infection and the clinician gave empirical anti-infectious medication for expectant treatment. During the course of disease, surgical incision was performed to relieve suffocation and shortness of breath. The drainage fluids were detected with microbiological culture and molecular sequencing. Nanopore sequencing technology (NST) helped to identify the coinfection of Conclusion: This case presented a CNF patient with type 2 diabetes, successfully recovered after prompt microbial detection, precise anti-infectious treatment, and appropriate surgical intervention. It highlights the importance of recognizing pathogen by applying rapid microbiological detection, including NST, in acute and serious infectious disease.

Indexed as

case reportcervicothoracic necrotizing fasciitis (CNF)coinfectionnanopore sequencing technology (NST)necrotizing soft-tissue infections (NSTIs)

Identifiers

PMID39497848
PMCPMC11533812

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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.