A Novel Report on the Compassionate Use of Baricitinib in Treating a Pediatric Patient With Severe Symptoms of COVID-19 Infection
Bibliographic record
Abstract
Since the outbreak of the pandemic coronavirus disease 2019 (COVID-19), there has been an increasing need for treatment to decrease morbidity and mortality of patients presenting with severe disease symptoms. There has been increasing evidence to suggest that the pathophysiological basis is a severe inflammatory response that resembles the cytokine release syndrome. Current strategies to counteract this involve modifiers of the immune response such as interleukin (IL)-6 receptor blockers and Janus kinase (JAK) inhibitors. An example of a JAK inhibitor is baricitinib. In this case, we present a 17-year-old female admitted with severe COVID-19 symptoms, who was placed on high-flow nasal cannula and started on azithromycin and hydroxychloroquine, which were standard of care at the time. Due to the worsening of symptoms, she was given baricitinib for compassionate use. There was a rapid improvement in clinical and imaging findings, and the patient was discharged from the hospital within 8 days of admission. This study is fascinating because there are very limited studies published on the benefits of baricitinib in managing patients with severe symptoms of COVID-19 especially in the pediatric population, and the rapidity in recovery time was remarkable.
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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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".