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Record W3130126061 · doi:10.1056/nejmoa2100433

Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19

2021· article· en· W3130126061 on OpenAlexfundno aff

Bibliographic record

VenueNew England Journal of Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersChildren's Hospital of PittsburghNIHR Imperial Biomedical Research CentreHorizon 2020Health Research Council of New ZealandNational Health and Medical Research CouncilCanadian Institutes of Health ResearchUniversité de Versailles Saint-Quentin-en-YvelinesMinderoo FoundationKing Abdullah International Medical Research CenterAssistance publique-Hôpitaux de ParisUniversiteit AntwerpenUniversitätsklinikum JenaRadboud Universitair Medisch CentrumQueen's UniversityFiona Stanley HospitalInstitut National de la Santé et de la Recherche MédicaleUniversité Pierre et Marie CurieMinistère des Affaires Sociales et de la SantéSeventh Framework ProgrammeQueen's University BelfastKing Saud bin Abdulaziz University for Health ScienceEisaiUniversity of OxfordMedical Research CouncilDepartment of Health and Social CareHealth Research BoardNational Institute for Health and Care ResearchCRCMiningUniversité de SherbrookeWellcome TrustUniversity College DublinWater Environment and Reuse FoundationFP7 HealthUniversity of TorontoHelsingin YliopistoUniversity of AlbertaEuropean CommissionUniversity of PittsburghBreast Cancer Research FoundationAmgenNational Institute of General Medical SciencesUniversité Laval
KeywordsInterquartile rangeMedicineTocilizumabOdds ratioIntensive care unitConfidence intervalInternal medicineRheumatoid arthritis

Abstract

fetched live from OpenAlex

BACKGROUND: The efficacy of interleukin-6 receptor antagonists in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear. METHODS: We evaluated tocilizumab and sarilumab in an ongoing international, multifactorial, adaptive platform trial. Adult patients with Covid-19, within 24 hours after starting organ support in the intensive care unit (ICU), were randomly assigned to receive tocilizumab (8 mg per kilogram of body weight), sarilumab (400 mg), or standard care (control). The primary outcome was respiratory and cardiovascular organ support-free days, on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support to day 21. The trial uses a Bayesian statistical model with predefined criteria for superiority, efficacy, equivalence, or futility. An odds ratio greater than 1 represented improved survival, more organ support-free days, or both. RESULTS: Both tocilizumab and sarilumab met the predefined criteria for efficacy. At that time, 353 patients had been assigned to tocilizumab, 48 to sarilumab, and 402 to control. The median number of organ support-free days was 10 (interquartile range, -1 to 16) in the tocilizumab group, 11 (interquartile range, 0 to 16) in the sarilumab group, and 0 (interquartile range, -1 to 15) in the control group. The median adjusted cumulative odds ratios were 1.64 (95% credible interval, 1.25 to 2.14) for tocilizumab and 1.76 (95% credible interval, 1.17 to 2.91) for sarilumab as compared with control, yielding posterior probabilities of superiority to control of more than 99.9% and of 99.5%, respectively. An analysis of 90-day survival showed improved survival in the pooled interleukin-6 receptor antagonist groups, yielding a hazard ratio for the comparison with the control group of 1.61 (95% credible interval, 1.25 to 2.08) and a posterior probability of superiority of more than 99.9%. All secondary analyses supported efficacy of these interleukin-6 receptor antagonists. CONCLUSIONS: In critically ill patients with Covid-19 receiving organ support in ICUs, treatment with the interleukin-6 receptor antagonists tocilizumab and sarilumab improved outcomes, including survival. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.).

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.401
Teacher spread0.366 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations2,032
Published2021
Admission routes1
Has abstractyes

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