The Impact on COVID-19 by Intravenous Bevacizumab Used for Hereditary Hemorrhagic Telangiectasia: A Case Report
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) continues as an infectious pandemic. With emphasis on mitigating its impact globally, strategies have been emphasized on prevention to treatment in severe cases. As for pharmacotherapies, many have been researched, with a few being recommended for patients with COVID-19 depending upon their severity. Bevacizumab, a recombinant monoclonal antibody often used for oncological disease and rare genetic disorders, has gained attention in combatting COVID-19 due to the pharmacotherapy's ability to inhibit vascular endothelial growth factor A (VEGF-A). VEGF has been identified as significantly upregulated in the lungs of persons who have died of COVID-19, raising interest for VEGF to be a potential target for patients with COVID-19. We present a case of a patient who was admitted due to complications of a rare genetic disorder, called hereditary hemorrhagic telangiectasia (HHT), warranting intravenous bevacizumab, who subsequently was diagnosed with COVID-19 pneumonia. We discuss the patient's outcome and contribute to the growing potential of bevacizumab in the treatment of COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".