Initial Guidance on Use of Monoclonal Antibody Therapy for Treatment of Coronavirus Disease 2019 in Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: In November 2020, the US Food and Drug Administration (FDA) provided Emergency Use Authorizations (EUA) for 2 novel virus-neutralizing monoclonal antibody therapies, bamlanivimab and REGN-COV2 (casirivimab plus imdevimab), for the treatment of mild to moderate coronavirus disease 2019 (COVID-19) in adolescents and adults in specified high-risk groups. This has challenged clinicians to determine the best approach to use of these products. METHODS: A panel of experts in pediatric infectious diseases, pediatric infectious diseases pharmacy, pediatric intensive care medicine, and pediatric hematology from 29 geographically diverse North American institutions was convened. Through a series of teleconferences and web-based surveys, a guidance statement was developed and refined based on review of the best available evidence and expert opinion. RESULTS: The course of COVID-19 in children and adolescents is typically mild and there is no high-quality evidence supporting any high-risk groups. There is no evidence for safety and efficacy of monoclonal antibody therapy for treatment of COVID-19 in children or adolescents, limited evidence of modest benefit in adults, and evidence for potential harm associated with infusion reactions or anaphylaxis. CONCLUSIONS: Based on evidence available as of December 20, 2020, the panel suggests against routine administration of monoclonal antibody therapy (bamlanivimab, or casirivimab and imdevimab), for treatment of COVID-19 in children or adolescents, including those designated by the FDA as at high risk of progression to hospitalization or severe disease. Clinicians and health systems choosing to use these agents on an individualized basis should consider risk factors supported by pediatric-specific evidence and ensure the implementation of a system for safe and timely administration that does not exacerbate existing healthcare disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".