The COVID-19 pandemic and its skin effects
Bibliographic record
Abstract
OBJECTIVE: To review the current literature on cutaneous diseases associated with the global coronavirus disease 2019 (COVID-19) pandemic, and to provide a general overview for family physicians of dermatologic presentations associated with COVID-19. QUALITY OF EVIDENCE: . Additional terms were personal protective equipment (PPE), hand hygiene, and psychosocial factors affecting skin diseases. Only English-language literature was reviewed. Evidence ranged from levels I to III. MAIN MESSAGE: Coronavirus disease 2019 is associated with a range of cutaneous presentations through direct infection with severe acute respiratory syndrome coronavirus 2, such as maculopapular, vesicular, pseudo-chilblain, livedoid, necrotic, urticarial, and Kawasaki-like rashes. Indirect presentations secondary to behavioural modifications are associated with use of personal protective equipment and sanitization procedures. Furthermore, psychosocial factors and stress associated with the pandemic also exacerbate pre-existing skin conditions. CONCLUSION: The COVID-19 pandemic has increased rates of dermatologic conditions through direct infection, behavioural changes, and association with psychosocial factors. As the incidence of COVID-19 increases, family physicians should be well equipped to diagnose and manage dermatologic presentations as they change within the context of the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.006 | 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 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".