Drug Provocation Testing in Allergological Work‐Up of <scp>DRESS</scp> : A Retrospective Real‐Life Experience From a Specialist Centre
Bibliographic record
Abstract
BACKGROUND: In drug reaction with eosinophilia and systemic symptoms (DRESS), cutaneous tests help to identify the responsible drug. However, uncertainty may persist and drug provocation tests (DPTs) could enable contraindications for essential drugs to be avoided. OBJECTIVES: To report the experience of DPTs in a series of DRESS patients managed in a referral centre for severe drug reactions. METHODS: We performed a single-centre retrospective study (October 2015-October 2021) of patients referred for an allergological work-up after DRESS. RESULTS: Seventy-five patients were included (49 females, mean age 55 years, 212 suspected drugs). Cutaneous tests, realised in all patients, were positive in 38, and DPTs were performed in 39 patients (65 drugs: 41 suspected and 24 alternative drugs, all with negative cutaneous tests). DPTs were performed regardless of DRESS severity or RegiSCAR score, at full dose (FD) for 40 drugs and graduated dose (from 1/100 FD) for 25 drugs. The objective was always to avoid contra-indications or to propose alternative drugs. Mild reactions occurred following challenge with 13 drugs (all suspected drugs), of which 6 could be continued. DPTs were more often positive when more drugs were suspected or reintroduced, but less often when a high-notoriety drug was suspected and avoided. CONCLUSIONS: DPTs can be considered in DRESS by experienced teams to avoid unnecessary contraindications for essential drugs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".