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Record W2171378113 · doi:10.1517/14740330902933736

Immune mediation of hypersensitivity adverse drug reactions: implications for therapy

2009· review· en· W2171378113 on OpenAlexaff
Michael Rieder

Bibliographic record

VenueExpert Opinion on Drug Safety · 2009
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWestern UniversityChildren’s Health Research InstituteChildren's Hospital of Western Ontario
Fundersnot available
KeywordsMedicineDrugImmune systemAdverse effectHaptenImmunologyPharmacologyAntibody

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.394
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2009
Admission routes1
Has abstractyes

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