Emergency department use of monoclonal antibody therapy in high risk COVID positive patients
Bibliographic record
Abstract
Background: There have been many perceived barriers to the implementation of the mass use of monoclonal antibody therapy following the Food and Drug Administration’s Emergency Use Authorization in November 2020. These barriers include identifying eligible patients, physical resources including trained staff members, space, and materials for the administration away from others to reduce transmission, and cost of the resources. However, Lakeland Regional Health was able to create a safe and efficient protocol to administer Bamlanivimab in the treatment of high risk COVID positive patients and initiate this proposed pathway within 24 hours of receipt of the first shipment of medication.Methods: Critical to the development and success of this protocol was a multi-disciplinary approach focused on identifying and utilizing preexisting resources to ensure safe and efficient administration of this treatment to as many eligible patients as possible. Another crucial aspect was the utilization of the emergency department providers for identifying high risk eligible patients and as a safe and effective treatment setting.Results: This article is intended to demonstrate a best practice pathway to identify and administer Bamlanivimab, or similar treatments, and will not discuss outcomes or efficacy of the medication. To date Lakeland Regional Health has successfully treated over 1,000 high risk COVID-19 positive patients within our community.Conclusions: By identifying and utilizing similar resources and pathways available at individual medical centers, it is possible to safely and efficiently treat high risk COVID positive patients with monoclonal antibody therapy on a large scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".