Antifungal hypersensitivity reactions and cross-reactivity patterns
Bibliographic record
Abstract
PURPOSE OF REVIEW: The goal of this article is to provide an updated understanding and evidence-based approach where possible for antifungal hypersensitivity. This includes recognition of clinical phenotype, implications for cross-reactivity and diagnostic, and management strategy for immediate and delayed hypersensitivity reactions. RECENT FINDINGS: Antifungal hypersensitivity reactions can be classified according to their latency (immediate or delayed) and clinical phenotype. The majority of the cases described in the literature are delayed T-cell mediated reactions of various severities but immediate reactions consistent with non-Immunoglobulin E (IgE)-mediated mast cell activation and IgE-mediated reactions have also been described. Ancillary information such as skin testing, drug challenge and ex vivo experimental approaches can aid causality assessments and inform antifungal class cross-reactivity, which help optimize antifungal prescribing and stewardship. SUMMARY: This review will update the clinician on mechanisms of drug hypersensitivity as well as providing a structured approach to the recognition, diagnosis and management of antifungal hypersensitivity reaction.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".