Early Covid-19 Treatment With SARS-CoV-2 Neutralizing Antibody Sotrovimab
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it