Immune mediation of hypersensitivity adverse drug reactions: implications for therapy
Bibliographic record
Abstract
Adverse drug reactions are among the top causes of death in the developed world, and among the spectrum of adverse drug reactions, drug hypersensitivity is a principal contributor to serious adverse drug events. The pathophysiology of drug hypersensitivity remains incompletely understood, but seems to involve the initial recognition of a drug or metabolite by the immune system followed by an immune response that determines the clinical manifestations. At present, there are two competing theories for how immune recognition occurs: the Hapten Hypothesis in which drug hapten-carrier association is the key driver for immune recognition and the Pharmacological Interference Concept that postulates direct recognition of drugs by low affinity association with the T cell receptor. The Danger Hypothesis provides a potentially important addition to the Hapten Hypothesis. Therapy for drug hypersensitivity has traditionally involved excellent supportive care. Although corticosteroids and intravenous immunoglobulin have both been used as immunomodulatory therapy, there is no robust evidence supporting the efficacy of their therapy for drug hypersensitivity. Recent advances in molecular biology and genomic pharmacology offer previously unappreciated opportunities to clarify the controversies surrounding drug hypersensitivity and to better diagnose, treat and, it is hoped, prevent drug hypersensitivity in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".