Evidence map›Paper›PMID 37529561›Full record

ArticleJournal of applied statistics2023

Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors.

Teng Chen, Paweł Polak, Stanislav Uryasev

Open access · greenAbstract read
In one paragraph

Article in Journal of applied statistics, 2023. 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
0.2field-weighted citation impact, top 53% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  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

3 authors at 1 institution in 1 country.

Teng ChenDepartment of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA.
Paweł PolakDepartment of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA.ORCID https://orcid.org/0000-0001-6645-015X
Stanislav UryasevDepartment of Applied Math & Statistics, Stony Brook University, Stony Brook, NY, USA.ORCID https://orcid.org/0000-0001-6950-3966
Stony Brook University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several

Indexed as

62-0762P10cluster analysisCOVID-19generalized additive modelnonparametric logistic regressionspline interpolation

Identifiers

PMID37529561
PMCPMC10388828
OpenAlexW4229065810

What Socratic 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 Socratic graph.