Tools to improve the diagnosis and management of T-cell mediated adverse drug reactions
Bibliographic record
Abstract
Delayed drug T-cell immune-mediated hypersensitivity reactions have a large clinical heterogeneity varying from mild maculopapular exanthema (MPE) to severe cutaneous adverse reactions (SCARs) such as acute generalized exanthematous pustulosis (AGEP), drug reaction with eosinophilia and systemic symptoms (DRESS) and severe skin necrosis and blistering as seen in Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Given the knowledge gaps related to the immunopathogenesis of these conditions, the absence of validated diagnostic tools and the significant associated morbidity and mortality, patients with SCARs often have limited drug choices. We performed a comprehensive review aiming to evaluate in vivo diagnostic tools such as delayed intradermal skin and patch testing and ex vivo/in vitro research assays such as the lymphocyte transformation test (LTT) and the enzyme-linked ImmunoSpot (ELISpot) assay. We searched through PubMed using the terms “drug allergy,” “ in vivo ” and “ ex vivo ” for original papers in the last 10 years. A detailed meticulous approach adapted to the various clinical phenotypes is recommended for the diagnostic and management of delayed drug hypersensitivity reactions. This review highlights the current diagnostic tools for the delayed drug hypersensitivity phenotypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.002 |
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".