Current understanding of the mechanisms of idiosyncratic drug-induced agranulocytosis
Bibliographic record
Abstract
INTRODUCTION: Idiosyncratic drug-induced agranulocytosis (IDIAG) is a life-threatening adverse reaction characterized by an absolute neutrophil count < 500 cells/μl of blood. It shares many of the characteristics of other idiosyncratic drug reactions (IDRs), and this presumably reflects mechanistic similarities. AREAS COVERED: This review describes the evidence for mechanistic hypotheses of IDIAG and new hypotheses are explored. EXPERT OPINION: The characteristics of IDIAG are most consistent with an immune mechanism. Where genetic studies have been done, the genes associated with an increased risk of IDIAG are either human leukocyte antigen genes or other genes associated with the immune response, which provides further evidence for an immune mechanism. There is evidence that the immune response leading to most IDRs is triggered by reactive metabolites of the offending drug, and most drugs that are associated with IDIAG are either known to be oxidized to a reactive metabolite by neutrophils or have a functional group that has the potential to be easily oxidized to a reactive metabolite. There is new evidence that drugs that cause IDRs including IDIAG can activate inflammasomes. Thus, the ability of a drug to be oxidized to a reactive metabolite by neutrophils and to activate inflammasomes may be useful biomarkers to predict IDIAG risk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".