The pathophysiology of drug hypersensitivity
Bibliographic record
Abstract
Drug Hypersensitivity reactions (DHRs) - either Immediate or Delayed – are among the most feared adverse effects of drug therapy. While DHRs are perhaps one-sixth of all ADRs, they are among the most problematic given that they are unpredictable, often severe and are major disruptors of therapy both currently and in the future. A key problem with addressing DHRs is the lack of a clear understanding of their pathophysiology, which appears quitomplex. The immune system in a clearly a key mediator of DHRs, but while IgE has been identified as a core element in the pathophysiology of immediate DHRs, much less is known for delayed DHRs. The classical hypotheses for the pathophysiology are the Hapten Hypothesis, the Pharmacologic Interference Hypothesis and the Danger Hypothesis. More recently the Altered Peptide Repertoire Hypothesis has been suggested. Recent work demonstrating the potential contributions of viral infection and inflammasome activation have led us to propose the Cross-Reactivity Hypothesis as a unifying platform bring the hypotheses together and to help understand potential role(s) other factors in the dysregulated immune activation resulting in delayed DHRs. Hence delayed DHRs may begin with metabolism of the drug to a reactive metabolite with haptenation and activation of the immune system not as a solitary players but rather as part of a proinflammatory milieu driven by pathogen or danger signals. There is an urgent need for research to better define the pathophysiology of delayed DHRs to inform best approaches to diagnose, treat and ideally prevent these serious ADRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".