Bibliographic record
Abstract
As the world faces the coronavirus disease 2019 (COVID-19) pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, concerns have been raised that asthma patients could be at increased risk of SARS-CoV-2 infection and disease severity. However, it appears that asthma is not an independent risk factor for both. Furthermore, asthma is not over-represented in hospitalised patients with severe pneumonia due to SARS-CoV-2 infection and there was no increased risk of asthma exacerbations triggered by SARS-CoV-2. There is accumulating evidence that asthma phenotypes and comorbidities are important factors in evaluating the risk for SARS-CoV-2 infection and disease severity, as findings suggest that Th2-high inflammation may reduce the risk of SARS-Cov-2 infection and disease severity in contrast to increased risk in patients with Th2-low asthma. The use of inhaled corticosteroids (ICS) is safe in asthma patients with SARS-CoV-2 infection. Furthermore, it has been proposed that ICS may confer some degree of protection against SARS-CoV-2 infection and the development of severe disease by reducing the expression of angiotensin converting enzyme-2 and transmembrane protease serine in the lung. In contrast, chronic or recurrent use of systemic corticosteroids before SARS-CoV-2 infection is a major risk factor of poor outcomes and worst survival in asthma patients. Conversely, biological therapy for severe allergic and eosinophilic asthma does not increase the risk of being infected with SARS-CoV-2 or having worse COVID-19 severity. In the present review we will summarise the current literature regarding asthma and COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".