Monoclonal antibodies as COVID-19 prophylaxis therapy in immunocompromised patient populations
Bibliographic record
Abstract
OBJECTIVES: The objective of this review was to examine the latest literature regarding the effectiveness of monoclonal antibodies as COVID-19 prophylaxis therapy for immunocompromised patient populations. METHODS: Literature review of published real-world and randomized control trials (RCTs) from 2020 to May 2023. RESULTS: COVID-19 is highly transmissible with potentially serious health outcomes, underscoring the need for effective prevention and treatment strategies. Vaccines are highly effective at preventing COVID-19 for the general population; however, efficacy is often impaired in immunocompromised patients given insufficient response to initial exposure and/or memory for secondary exposures. Some individuals may also have contraindications to vaccination. As such, additional protective measures are needed to bolster the immune response in these populations. Monoclonal antibodies have been effective at bolstering immune system responses to COVID-19 among immunocompromised patients; however, they are proving ineffective against the most recent Omicron strains (BA.4 and BA.5). CONCLUSION: Several studies have investigated the efficacy of monoclonal antibodies as pre- and post-prophylaxis for COVID-19. Historical evidence is promising; however, new variants of concern are proving challenging for currently available regimens.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".