Functionality of Monoclonal Antibody Therapy in SARS-CoV-2
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic emerged as a world crisis in 2019 and started a global search for optimal therapeutic regimen including vaccines, antiviral agents, and recently monoclonal antibody therapy. Clinical trials are currently underway for the efficacy of several neutralizing monoclonal antibodies against COVID-19. The evolution of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with immune evasion capacity has created a challenge for the healthcare workers with urgent need for prospective studies to determine functionality of monoclonal antibody therapy and their role in the reduction of hospitalization for disease severity. Herein, we report three cases of COVID-19 during the beginning of the spread of Omicron variants that were hospitalized after treatment with monoclonal antibody therapy in the emergency department. All the patients showed progression of the disease on imaging and were treated with dexamethasone, remdesivir and anticoagulation based on the symptoms and contraindications. Two of the patients recovered and were discharged with out-patient follow-up; however, one patient expired in the hospital. Monoclonal antibody therapy is a promising treatment to limit the progression of COVID-19 and reduce the hospital strain specifically in small community hospitals. Limited information is available about their efficacy in the new viral variants. These cases emphasize the need of future prospective study and randomized controlled trials to illustrate the utilization of monoclonal antibodies as a therapeutic modality in patients infected with the variants of SARS-CoV-2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".