Evidence map›Paper›PMID 39569002›Full record

ArticleFrontiers in microbiology2024

Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model.

Weiheng Kong, Yujia Liu, Wang Li, Keyi Yang, Lixin Yu, Guangyu Jiao

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2024. 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. Article
  2. Review
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.

Weiheng Kong *Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Yujia Liu *College of Traditional Chinese Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Wang LiDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Keyi YangDepartment of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Lixin YuDepartment of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Guangyu JiaoDepartment of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: By extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO Methods: This retrospective study included 384 patients with COVID-19, whose baseline information, laboratory indicators, oxygenation-related parameters, and non-enhanced chest CT images were collected. Utilizing the PaO Results: There were no statistically significant differences in baseline characteristics among the groups. Radiomic features showed differences in all 4 groups, while the differences in laboratory indicators were inconsistent, with some PaO Conclusion: This study found that the early chest CT radiomic features of COVID-19 patients are strongly associated not only with early laboratory indicators but also with the lowest PaO

Indexed as

artificial intelligencechest CT radiomic featuresCOVID-19laboratory indicatorsmachine learningPaO2/FiO2SARS-CoV-2

Identifiers

PMID39569002
PMCPMC11576442

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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.