Immunobridging for Pemivibart, a Monoclonal Antibody for Prevention of Covid-19
Bibliographic record
Abstract
p o n d e n c e Immunobridging for Pemivibart, a Monoclonal Antibody for Prevention of Covid-19To the Editor: On March 22, 2024, the Food and Drug Administration issued Emergency Use Authorization (EUA) for the monoclonal antibody pemivibart as preexposure prophylaxis for Covid-19 in certain adults and adolescents with moderate-to-severe immunocompromise.The EUA was issued on the basis of safety and immunobridging data from the CANOPY trial (ClinicalTrials.govnumber, NCT06039449), which included two cohorts: an open-label cohort of 306 persons with moderate-to-severe immunocompromise, who received an initial intravenous infusion of a single 4500-mg dose of pemivibart followed by a second infusion at the same dose approximately 90 days later, and a placebo-controlled cohort of persons without immunocompromise, who were randomly assigned to receive either pemivibart or placebo on the same schedule.Additional information is provided in the protocol and Supplementary Appendix, both of which are available with the full text of this letter at NEJM.org.The analysis of the primary end point was performed by means of an immunobridging method, which allowed for a comparison of the neutralizing antibody titers calculated for pemivibart with those calculated for the monoclonal antibody adintrevimab.Adintrevimab had shown efficacy against the B.1.617.2 (delta) variant but
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".