Notes from the Field: The Combined Effects of Tocilizumab and Remdesivir in a Patient with Severe COVID-19 and Cytokine Release Syndrome
Bibliographic record
Abstract
SARS-CoV-2 is known to cause severe bilateral pneumonia and acute respiratory distress syndrome or COVID-19 in patients, which can be debilitating and even fatal. With no drugs or vaccines available yet, a wide range of treatment regimens used are being repurposed. The need of the hour is to analyze various currently available regimens and devise a treatment plan that is most effective for COVID-19. Here we describe the case of a 68-year-old man with hypertension and diabetes, exhibiting symptoms of cough and shortness of breath, who presented at the emergency department of our hospital. Chest computed tomography revealed bilateral ground glass opacities that were indicative of COVID-19, and a computed tomography score of 24 was indicative of severe pulmonary pneumonia. He tested positive for COVID-19. His treatment regimen included the use of convalescent plasma, oxygen therapy, steroids, high-dose antibiotics, broad-spectrum antiviral remdesivir, and anti-interleukin-6 monoclonal antibody (Tocilizumab) at various stages of the disease. Oxygen supplementation was required at the time of admission. The patient initially developed a cytokine release storm, and oxygen supplementation was initiated to manage his condition. Supportive care and multiple treatment regimens were used to successfully recover the patient's health. With a rapid increase in number of confirmed cases worldwide, COVID-19 has become a major challenge to our health care system. With no available vaccines currently, the establishment of a combination of therapeutic drugs that effectively reduce disease progression is of utmost importance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".