Diagnosing and Managing Drug Reaction With Eosinophilia and Systemic Symptoms (DRESS) Amidst Remaining Uncertainty
Bibliographic record
Abstract
Drug reaction with eosinophilia and systemic symptoms (DRESS), a severe cutaneous adverse reaction, represents a diagnostic and therapeutic challenge due to its varied, evolving clinical presentation, complex pathophysiology, and potential for severe systemic involvement. This article explores DRESS syndrome through 3 illustrative cases from diverse populations and with different background comorbidities. Cases highlight different challenges in DRESS care, including (1) the need for early diagnosis and severity scoring, (2) identification of offending drugs and risk stratification to consider a possible drug challenge, and (3) best practice management including long-term monitoring for emergent autoimmunity. Recent developments in our understanding of clinical spectrum of disease, genomic and nongenomic biomarkers, severity groupings, and pharmacological and longer-term management strategies are described. Critical gaps remain in several of these domains, particularly in vulnerable groups such as the immune-compromised. In the absence of robust evidence, we aim in this article to assist attending clinicians with current expert opinion in DRESS management. Finally, we highlight areas for further research needed to improve the clinical care and outcomes of DRESS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".