Possible effect of epinephrine in minimizing COVID-19 severity: a review
Bibliographic record
Abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) shows a wide range of severity, ranging from an asymptomatic presentation to a severe illness requiring intensive care unit admission. Identification of a strategy to manage the severity of this disease will not only help to reduce its case fatality but also help to remove some of the burden from the already overwhelmed health care systems. While successful management of symptoms in general is important, identifying measures to modify the severity of the illness is a key factor in the fight against this pandemic. METHODS: This paper presents a short literature review to suggest a new treatment modality for COVID-19. RESULTS: COVID-19 is less severe and rarely fatal in children than in adults, which could be caused by greater fluctuations of plasma epinephrine in children. Our literature survey endorses this hypothesis according to both the epidemiological and immunological findings. CONCLUSION: Application of epinephrine pulses with a specific amplitude may be considered an intervention to minimize the severity 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".