Evidence map›Paper›PMID 40842538›Full record

ReviewFrontiers in medicine2025

Critical care ultrasound: development, evolution, current and evolving clinical concepts in critical care medicine.

Jing Su, Xin Tie, Ying Wei, Ran Zhou, Tongjuan Zou, Yao Qin, Xueying Zeng, Yi Li, Wanhong Yin

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2025. 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
–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

2 citing papers in PubMed.

  1. Review
  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

9 authors.

Jing SuDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Xin TieDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Ying WeiDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Ran ZhouDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Tongjuan ZouDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yao QinDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Xueying ZengDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yi LiDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Wanhong YinDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Critical care ultrasound (CCUS) has emerged as a vital tool in modern critical care medicine (CCM), revolutionizing the assessment and management of critically ill patients. CCUS provides real-time insights into patient conditions and enables physicians to analyze the underlying mechanisms and pathophysiology of critical illnesses based on established theories and clinical needs, ultimately visualizing the phenotypes of patients and guiding clinical practice. The innovations of wearable ultrasound and the incorporation of artificial intelligence are further reshaping and broadening its application. This review underscores the importance of CCUS as an integral component of CCM, highlighting its development, current applications, and future directions. In addition, questions are raised regarding the standard training and guidelines of CCUS needing to be addressed in the near future.

Indexed as

challengescritical caredevelopmentevolutionultrasound

Identifiers

PMID40842538
PMCPMC12364920

What OpenQuestion 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 OpenQuestion graph.