Exploring cytokine outputs for ex vivo diagnostics in drug reaction with eosinophilia and systemic symptoms (DRESS)
Bibliographic record
Abstract
Background In an exploratory study to assess the potential to individualize T-cell diagnostics in antibiotic-associated severe T-cell mediated hypersensitivity, we focused on drug reaction with eosinophilia and systemic symptoms (DRESS) and the related cytokine outputs IL-4 and IL-5. Methods Patients with well-phenotyped RegiSCAR ≥4 DRESS, positive intradermal skin testing, and a previous negative IFN-γ Enzyme-Linked ImmunoSpot (ELISpot) assay were prospectively recruited. We specifically performed an ELISpot assay with IL-4 and IL-5 cytokine outputs. As comparative controls, these cytokine outputs were performed simultaneously in patients with a positive ex vivo IFN-γ release ELISpot result. Results Four antibiotic-associated DRESS cases were included. The IL-4 and IL-5 output ELISpot assay demonstrated various results among these patients, with at least 1 cytokine present in all the cases. As for the 2 controls with known positive IFN-γ release, compared to the IFN-γ secretion, the cytokine output using IL-4 and IL-5 showed an increased positivity. Conclusion In patients where the early response has suggested a TH2 response such as DRESS, IL-4 and IL-5 cytokine outputs could present an investigational advantage, including when IFN-γ is negative. In the future, larger prospective studies are required to understand the role of varied cytokine outputs in T-cell-mediated hypersensitivities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".