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Record W4413336604 · doi:10.1093/cid/ciaf355

2025 Clinical Practice Guideline Update by the Infectious Diseases Society of America on the Treatment and Management of COVID-19: Infliximab

2025· article· en· W4413336604 on OpenAlexaff
Nandita R. Nadig, Adarsh Bhimraj, Kelly Cawcutt, Kathleen Chiotos, Amy Dzierba, Arthur Y Kim, Greg S. Martin, Jeffrey C Pearson, Amy Hirsch Shumaker, Lindsey R. Baden, Roger Bedimo, Vincent Chi‐Chung Cheng, Kara W Chew, Eric S. Daar, David V. Glidden, Erica Hardy, Steven C. Johnson, Jonathan Z. Li, Christine E. MacBrayne, Mari Nakamura, Laura E. Riley, Robert W. Shafer, Shmuel Shoham, Pablo Tebas, Phyllis C. Tien, Jennifer Loveless, Yngve Falck–Ytter, Rebecca L. Morgan, Rajesh T. Gandhi

Bibliographic record

VenueClinical Infectious Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsMcMaster UniversityImpact
FundersNational Cancer InstituteNational Institutes of HealthInfectious Diseases Society of America
KeywordsGuidelineMedicineCoronavirus disease 2019 (COVID-19)InfliximabGrading (engineering)Intensive care medicineMEDLINEFamily medicine2019-20 coronavirus outbreakInfectious disease (medical specialty)DiseaseInternal medicinePathology

Abstract

fetched live from OpenAlex

This article provides a focused update to the clinical practice guideline on the treatment and management of patients with coronavirus disease 2019 (COVID-19), developed by the Infectious Diseases Society of America. The guideline panel presents a recommendation on the use of infliximab in hospitalized adults with severe or critical COVID-19. The recommendation is based on evidence derived from a systematic literature review and adheres to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0120.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.444
Teacher spread0.394 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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