Drug Reaction with Eosinophilia and Systemic Symptoms (DReSS)/Drug-Induced Hypersensitivity Syndrome (DiHS)—Readdressing the DReSS
Bibliographic record
Abstract
Drug reaction with eosinophilia and systemic symptoms (DReSS), also known as drug-induced hypersensitivity syndrome (DiHS), is a severe, systemic, T cell mediated drug reaction with combinations of cutaneous, hematologic, and internal organ involvement. Pathogenesis of DReSS is multi-factorial, involving drug-exposure, genetic predisposition through specific human leukocyte antigen (HLA) alleles and metabolism defects, viral reactivation, and immune dysregulation. Clinical features of this condition are delayed, stepwise, and heterogenous, making this syndrome challenging to recognize and diagnose. Two sets of validated diagnostic criteria exist that can be employed to diagnose DReSS/DiHS. Methods to improve early recognition of DReSS and predict disease severity has been a recent area of research focus. In vitro and in vivo tests can be employed to confirm the diagnosis and help identify culprit drugs. The mainstay treatment of DReSS is prompt withdrawal of the culprit drug, supportive treatment, and immunosuppression depending on the severity of disease. We present a comprehensive review on the most recent research and literature on DReSS, with emphasis on pathogenesis, clinical features, diagnosis, confirmatory testing modalities, and treatment. Additionally, this summary aims to highlight the differing viewpoints on this severe disease and broaden our perspective on the condition known as DReSS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".