Evidence map›Paper›PMID 41574379›Full record

ArticleFrontiers in medicine2025

Machine learning-based approach to guide the choice between baricitinib and tocilizumab in critical COVID-19 pneumonia treatment: a retrospective cohort study.

Euijin Chang, Myung-Soo Kim, Se Yoon Park, Kyungsup Kwon, Hyeon Mu Jang, So Yun Lim, Seongman Bae, Jiwon Jung, Min Jae Kim, Yong Pil Chong and 7 more

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Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

17 authors.

Euijin Chang *Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Myung-Soo Kim *Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Se Yoon Park *Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea.
Kyungsup KwonDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Hyeon Mu JangDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
So Yun LimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Seongman BaeDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Jiwon JungDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Min Jae KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Yong Pil ChongDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sang-Oh LeeDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sang-Ho ChoiDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Yang Soo KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Gyucheol ChoiImpriMedKorea, Inc., Seoul, Republic of Korea.
Sungwon LimImpriMed, Inc., Mountain View, CA, United States.
Jamin KooImpriMedKorea, Inc., Seoul, Republic of Korea.
Sung-Han KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Clear guidance on choosing baricitinib (BCT) or tocilizumab (TCZ) for critical COVID-19 pneumonia remains limited. We developed machine-learning (ML) models to inform immunomodulator selection. Methods: We curated clinical data from patients with critical COVID-19 pneumonia admitted between January 2020 and June 2024. Development cohort ( Results: TCZ and BCT models achieved ROC-AUCs of 0.81 and 0.84, with test accuracies of 0.67 and 0.77, respectively. In test cohort, survival differed significantly between high- and low-risk strata for each agent. In Group II, mortality was significantly higher with BCT than TCZ (hazard ratio (HR) 2.32, Conclusion: ML models may support treatment selection between BCT and TCZ in patients with critical COVID-19 pneumonia. Prospective studies are warranted to assess whether model-guided choices improve survival and to validate generalizability across clinical settings.

Indexed as

baricitinibcoronavirus disease 2019machine-learningpneumoniatocilizumab

Identifiers

PMID41574379
PMCPMC12819291

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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.