Infection risk of dermatologic therapeutics during the COVID‐19 pandemic: an evidence‐based recalibration
Bibliographic record
Abstract
Recommendations were made recently to limit or stop the use of oral and systemic immunotherapies for skin diseases due to potential risks to the patients during the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) COVID-19 pandemic. Herein, we attempt to identify potentially safe immunotherapies that may be used in the treatment of cutaneous diseases during the current COVID-19 pandemic. We performed a literature review to approximate the risk of SARS-CoV-2 infection, including available data on the roles of relevant cytokines, cell subsets, and their mediators in eliciting an optimal immune response against respiratory viruses in murine gene deletion models and humans with congenital deficiencies were reviewed for viral infections risk and if possible coronaviruses specifically. Furthermore, reported risk of infections of biologic and non-biologic therapeutics for skin diseases from clinical trials and drug data registries were evaluated. Many of the immunotherapies used in dermatology have data to support their safe use during the COVID-19 pandemic including the biologics that target IgE, IL-4/13, TNF-α, IL-17, IL-12, and IL-23. Furthermore, we provide evidence to show that oral immunosuppressive medications such as methotrexate and cyclosporine do not significantly increase the risk to patients. Most biologic and conventional immunotherapies, based on doses and indications in dermatology, do not appear to increase risk of viral susceptibility and are most likely safe for use during the COVID-19 pandemic. The limitation of this study is availability of data on 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".