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Record W1807669271 · doi:10.1002/9780470921920.edm087

Idiosyncratic Drug Reactions

2012· other· en· W1807669271 on OpenAlexaff
Zbigniew W. Wojcinski, Jack Uetrecht

Bibliographic record

VenueEncyclopedia of Drug Metabolism and Interactions · 2012
Typeother
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMechanism (biology)DrugCircumstantial evidenceDrug reactionImmune systemPopulationBiologyComputational biologyImmunologyPharmacologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Idiosyncratic drug reactions (IDRs) are adverse drug reactions that are not related to the known pharmacological properties of the drug occur in only a small percentage of the population and do not show any apparent dose–response relationship. The unpredictable nature of IDRs together with the often serious adverse effects encountered pose a major health concern in the development and clinical usage of pharmaceuticals. The mechanism of IDRs is not clearly understood, and although several theories have been proposed, IDRs can be generally categorized mechanistically as either immune mediated or non‐immune‐mediated. There is circumstantial evidence that suggests that most idiosyncratic reactions are hypersensitivity reactions initiated by the formation of chemically reactive metabolites that bind to proteins and induce an immune‐mediated response. Investigations in animal models have had limited success owing to the unpredictability of IDRs in animals, which is similar to the situation in humans. However, IDRs in animals do share some similar characteristics and mechanisms observed in humans supporting the need for further study of animal models.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.271
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2012
Admission routes1
Has abstractyes

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