Lessons From the First Wave of the Pandemic: Skin Features of COVID-19 Can Be Divided Into Inflammatory and Vascular Patterns
Bibliographic record
Abstract
This review examines the clinical, morphological, and systemic factors related to coronavirus disease 2019 (COVID-19) cutaneous manifestations. The EMBASE, Medline, and Pubmed Central databases were searched from February 1, 2020 until April 25, 2020, using the search words "(COVID-19 OR SARS-CoV-2 OR coronavirus-19) AND (skin OR cutaneous OR dermatologic)". Cutaneous manifestations of COVID-19 were included. The cutaneous manifestations can be classified into 2 types. Patients with inflammatory reactions consisted of morbilliform, varicella-like, urticarial eruptions, and vesiculobullous manifestations. These manifestations were mainly found on the trunk, limbs, and faces of patients and had mainly positive COVID-19 polymerase chain reaction findings (97.7%). Furthermore, there were 516 patients with acral vascular lesions: chilblains, livedo lesions, cutaneous small-vessel vasculitis, and other noninflammatory purpura. These were often nonpruritic (88%) and not seen in severe disease (88.7%). The cutaneous lesions have potential for early diagnosis of COVID-19 and prevention of disease transmission. The implications of COVID-19 in the field of dermatology continue to evolve as more clinical data becomes available.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".