Evidence map›Paper›PMID 35594023›Full record

ArticleAdvances in experimental medicine and biology2022

Predicting the Disease Severity of Virus Infection.

Xin Qi, Li Shen, Jiajia Chen, Manhong Shi, Bairong Shen

Abstract read
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In one paragraph

Article in Advances in experimental medicine and biology, 2022. 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, top 74% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

5 authors at 3 institutions in 1 country.

Xin Qi *School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China. qixin@usts.edu.cn.
Li Shen *Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Jiajia ChenSchool of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China.
Manhong ShiCollege of Information and Network Engineering, Anhui Science and Technology University, Fengyang, China.
Bairong ShenInstitutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. bairong.shen@scu.edu.cn.
Sichuan University · CNSuzhou University of Science and Technology · CNAnhui Science and Technology University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has resulted in unprecedented burden on global health and economic systems, promoting worldwide efforts to understand, control, and fight the disease. Due to the wide spectrum of clinical severity, effective risk factors, biomarkers, and models for predicting disease severity and mortality in COVID-19 patients are urgently needed to provide guidance for clinical intervention and management. In this chapter, we first describe the infection features of different COVID-19 strains and the potential of clinical features, cytokine storm and biomarkers in predicting the severity of COVID-19 patients. We focus on how scoring systems, mathematical models and artificial intelligence (AI)-based models can promote the classification of COVID-19 severity at the population or individual level. Moreover, the development perspective of biomarkers and models for predicting the severity of COVID-19 is prospected. Therefore, this chapter highlights the clinical significance of biomarkers and models related to COVID-19 severity and provides important clues for improving the outcomes of COVID-19 patients, thereby facilitating timely disease assessment and precision medicine for individual COVID-19 patients.

Indexed as

COVID-19SARS-CoV-2Artificial IntelligenceBiomarkersHumansPandemicsSeverity of Illness IndexBiomarkersAI-based modelBiomarkerCOVID-19 severityCytokine stormMathematical modelRisk factorScoring system

Identifiers

PMID35594023
OpenAlexW4285264939

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.