Faculty Opinions recommendation of The measurement of drug-induced interferon γ-releasing cells and lymphocyte proliferation in severe cutaneous adverse reactions.
Bibliographic record
Abstract
BACKGROUND: The lymphocyte transformation test (LTT) is a standard laboratory method to identify culprit drugs in patients with a history of drug-induced non-immediate hypersensitivity and is mainly performed during the recovery phase. The measurement of drug-specific interferon γ (IFN-γ)-releasing cells has been introduced to confirm culprit drugs, even during the acute phase of drug allergy.OBJECTIVES: This study aimed to evaluate the capability of the enzyme-linked immunospot assay (ELISpot) to detect drug-specific IFN-γ-releasing cells during the acute phase and the capability of LTT to identify culprit drugs during the recovery phase in patients presenting with severe cutaneous adverse reactions (SCARs).METHODS: Peripheral blood mononuclear cells (PBMCs) from 23 SCAR patients were collected during the acute and recovery phases and assayed for drug-specific IFN-γ-releasing cells and lymphocyte proliferation, respectively.RESULTS: Drug-specific IFN-γ-releasing cells were detectable in 73.9% of SCAR subjects (55.6% and 85.7% in patients who were and were not taking systemic steroids, respectively), whereas LTT results were positive in 52.2% of SCAR subjects. The frequencies of drug-specific IFN-γ-releasing cells were significantly higher in patients with positive LTT than in those with negative LTT (260.1 ± 110.0 and 46.6 ± 20.7 cells/106 PBMCs, P = 0.01). A significant correlation between the results of the IFN-γ ELISpot assay and LTT was demonstrated (r = 0.65, P value < 0.01).CONCLUSION: The IFN-γ ELISpot assay could be a useful tool to identify culprit drugs in SCAR patients when culprit drug identification is urgently needed during the acute phase of drug allergy.© 2018 European Academy of Dermatology and Venereology. PMID: 29478292
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.336 | 0.311 |
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".