Evidence map›Paper›PMID 35693218›Full record

ReviewPrecision clinical medicine2021

Understanding the predictive value and methods of risk assessment based on coronary computed tomographic angiography in populations with coronary artery disease: a review.

Yiming Li, Kaiyu Jia, Yuheng Jia, Yong Yang, Yijun Yao, Mao Chen, Yong Peng

Open access · diamondAbstract readReview
In one paragraph

Review in Precision clinical medicine, 2021. 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
0.3field-weighted citation impact, top 43% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. 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

7 authors at 1 institution in 1 country.

Yiming LiDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Kaiyu JiaDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.ORCID https://orcid.org/0000-0002-8954-2636
Yuheng JiaDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Yong YangDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Yijun YaoDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Mao ChenDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Yong PengDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk assessment in coronary artery disease plays an essential role in the early identification of high-risk patients. However, conventional invasive imaging procedures all require long intraprocedural times and high costs. The rapid development of coronary computed tomographic angiography (CCTA) and related image processing technology has facilitated the formulation of noninvasive approaches to perform comprehensive evaluations. Evidence has shown that CCTA has outstanding performance in identifying the degree of stenosis, plaque features, and functional reserve. Moreover, advancements in radiomics and machine learning allow more comprehensive interpretations of CCTA images. This paper reviews conventional as well as novel diagnostic and risk assessment tools based on CCTA.

Indexed as

coronary artery diseasecoronary computed tomographic angiography (CCTA)prediction valuerisk assessment

Identifiers

PMID35693218
PMCPMC8982592
OpenAlexW3186158362

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.