Exploring Dermatological Complications of Drugs Used in Acute Respiratory Syndrome Coronavirus 2 Treatment: A Mini Review
Bibliographic record
Abstract
The COVID-19 pandemic has presented unprecedented challenges to governments and populations worldwide, triggering a global health crisis within months. In response, international health research organizations have highlighted various effects of chemical therapies, including dermatological complications. This mini-review explores the dermatological effects of drugs used in the treatment of COVID-19. This study examines the skin manifestations resulting from standard chemical treatments administered during the COVID-19 pandemic. Keywords were cross-referenced across multiple databases, including Web of Science, Scopus, PubMed, SID, Magiran, and Google Scholar. Search terms included COVID-19, coronavirus, SARS-CoV-2, chemical effects, neurological effects, and pandemic-related cardiac complications. The review encompasses a range of pharmaceuticals used in the treatment of COVID-19, such as hydroxychloroquine, remdesivir, azithromycin, dexamethasone, ribavirin/interferon, intravenous immunoglobulin (IVIG), oseltamivir, losartan, magnesium sulfate, and vitamin D3. Our investigation identifies various cutaneous manifestations, including rash, erythema, and ulceration. Additionally, early-onset nocturnal symptoms, somnolence, dyspnea, edema, arrhythmias, scleroderma, and other adverse effects are associated with the standard pharmacotherapy used to manage 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".