<b>Skin Rashes in Emergency Department Visits, Clinical Features, Differential Diagnoses, and Admission Rates: A Systematic Review</b>
Bibliographic record
Abstract
Objective: To systematically review the epidemiology, clinical features, differential diagnoses, and outcomes of dermatological conditions presenting to emergency departments. Methods: A systematic review was conducted according to PRISMA guidelines. PubMed, Scopus, Web of Science, and Google Scholar were searched for studies published between 2011 and 2024. Eligible observational studies reporting on dermatological presentations in emergency departments were included. Data extraction and quality assessment were performed using the Newcastle–Ottawa Scale, and results were synthesized narratively. Results: Ten studies from the United States, Turkey, Saudi Arabia, and Germany were included, with sample sizes ranging from 204 to over 11,000 patients. Infections, urticaria, eczema, and drug eruptions were the most common presentations. Stevens–Johnson syndrome, toxic epidermal necrolysis, and severe drug reactions accounted for most admissions. Over 90% of patients were discharged without hospitalization. Admission rates ranged from 6% to 18%. Seasonal variation was observed in Saudi Arabia, while the COVID-19 pandemic shifted consultation patterns in Turkey. Teledermatology show effectiveness in diagnostic support, particularly in resource-limited settings. Conclusion: Most dermatological emergency department visits are non-urgent and can be managed on an outpatient basis. Early recognition of severity markers is essential to prevent adverse outcomes. Integration of standardized triage systems, physician training, and teledermatology can improve diagnostic accuracy, reduce inappropriate ED utilization, and optimize patient management.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".