Considerations on biologicals for patients with allergic disease in times of the COVID‐19 pandemic: An EAACI statement
Bibliographic record
Abstract
The outbreak of the SARS-CoV-2-induced coronavirus disease 2019 (COVID-19) pandemic re-shaped doctor-patient interaction and challenged capacities of healthcare systems. It created many issues around the optimal and safest way to treat complex patients with severe allergic disease. A significant number of the patients are on treatment with biologicals, and clinicians face the challenge to provide optimal care during the pandemic. Uncertainty of the potential risks for these patients is related to the fact that the exact sequence of immunological events during SARS-CoV-2 is not known. Severe COVID-19 patients may experience a "cytokine storm" and associated organ damage characterized by an exaggerated release of pro-inflammatory type 1 and type 3 cytokines. These inflammatory responses are potentially counteracted by anti-inflammatory cytokines and type 2 responses. This expert-based EAACI statement aims to provide guidance on the application of biologicals targeting type 2 inflammation in patients with allergic disease. Currently, there is very little evidence for an enhanced risk of patients with allergic diseases to develop severe COVID-19. Studies focusing on severe allergic phenotypes are lacking. At present, noninfected patients on biologicals for the treatment of asthma, atopic dermatitis, chronic rhinosinusitis with nasal polyps, or chronic spontaneous urticaria should continue their biologicals targeting type 2 inflammation via self-application. In case of an active SARS-CoV-2 infection, biological treatment needs to be stopped until clinical recovery and SARS-CoV-2 negativity is established and treatment with biologicals should be re-initiated. Maintenance of add-on therapy and a constant assessment of disease control, apart from acute management, are demanded.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".