Early Covid-19 Treatment With SARS-CoV-2 Neutralizing Antibody Sotrovimab
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (Covid-19) disproportionately results in hospitalization and death in older patients and those with underlying comorbidities. Sotrovimab is a pan-sarbecovirus monoclonal antibody designed to treat such high-risk patients early in the course of disease, thereby preventing Covid-19 progression. Methods In this ongoing, multicenter, double-blind, phase 3 trial, nonhospitalized patients with symptomatic Covid-19 and at least one risk factor for disease progression were randomized (1:1) to an intravenous infusion of sotrovimab 500 mg or placebo. The primary efficacy endpoint was the proportion of patients with Covid-19 progression, defined as hospitalization longer than 24 hours or death, through day 29. Results In this preplanned interim analysis, which included an intent-to-treat population of 583 patients (sotrovimab, 291; placebo, 292), the primary efficacy endpoint was met. The risk of Covid-19 progression was significantly reduced by 85% (97.24% confidence interval, 44% to 96%; P = 0.002) with a total of three (1%) patients progressing to the primary endpoint in the sotrovimab group versus 21 (7%) patients in the placebo group. All five patients admitted to intensive care, including one who died by day 29, received placebo. Safety was assessed in 868 patients (sotrovimab, 430; placebo, 438). Adverse events were reported by 17% and 19% of patients receiving sotrovimab and placebo, respectively; serious adverse events were less common with sotrovimab (2%) versus placebo (6%). Conclusion Sotrovimab reduced progression of Covid-19 in patients with mild/moderate disease, was well tolerated, and no safety signals were identified. Funded by Vir Biotechnology, Inc. and GlaxoSmithKline; ClinicalTrials.gov NCT04545060
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".