Evidence map›Paper›PMID 34308253›Full record

ReviewChinese journal of academic radiology2022

Advances in medical imaging to evaluate acute respiratory distress syndrome.

Shan Huang, Yuan-Cheng Wang, Shenghong Ju

Abstract readReview
In one paragraph

Review in Chinese journal of academic radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Imaging the Lung in ARDS: A Primer.Respiratory care · 2024
    Review
  3. 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

3 authors.

Shan HuangDepartment of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.
Yuan-Cheng WangDepartment of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.
Shenghong JuDepartment of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute respiratory distress syndrome is a refractory respiratory syndrome with a high prevalence in the Intensive Care Unit. Though much improvement has been achieved over the last 50 decades, the disease continues to be under-recognized and under-treated, and its mortality remains high. Since the first report, the radiologic examination has been an essential part in evaluating this disease. Chest X-ray radiography and computed tomography are conventional imaging techniques in routine clinical practice. Other image modalities, including lung ultrasound, electrical impedance tomography, positron emission tomography, have demonstrated their respective advantages over recent years but have not yet been broadly applied in clinical practice. Among these modalities, computed tomography and its quantitative analysis have shown an irreplaceable power in diagnosis, intervention evaluation and prognostic prediction. In this review, we briefly introduced the basics of acute respiratory distress syndrome and summarized imaging advances. In addition, we focused on the computed tomography modality and highlighted the value of its quantitative assessment.

Indexed as

Acute respiratory distress syndromeComputed tomographyImaging advancesQuantitative analysis

Identifiers

PMID34308253
PMCPMC8286037

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.