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Record W1522242651 · doi:10.1002/jcph.85

The Predictive Value of the In Vitro Platelet Toxicity Assay (iPTA) for the Diagnosis of Hypersensitivity Reactions to Sulfonamides

2013· article· en· W1522242651 on OpenAlexaff
Abdelbaset A. Elzagallaai, Gideon Koren, Michael Rieder

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2013
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWestern University
Fundersnot available
KeywordsToxicityDrugIn vitroPredictive valuePharmacologyMedicineDrug reactionChemistryInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Drug hypersensitivity reactions (DHRs) are rare but potentially fatal adverse drug reactions (ADRs). A reliable test to diagnose DHRs would be a major advance in the clinical care for patients and in the evaluation of ADRs during drug development as well as for mechanistic studies of drug hypersensitivity. Available in vitro tests including the lymphocyte toxicity assay (LTA) have been used but are time-consuming, cumbersome, and expensive. We have developed a novel diagnostic test for DHRs, the in vitro platelet toxicity assay (iPTA). The aim of this study was to evaluate the predictive value of the iPTA in diagnosis of DHRs to sulfonamides. We recruited 66 individuals (36 DHS-sulfa patients and 30 healthy controls) to participate in the study. Blood samples were obtained and LTA and iPTA were performed in parallel. There was concentration-dependent toxicity in the cells of patients when incubated with the reactive hydroxylamine metabolite of sulfamethoxazole for both the LTA and iPTA (P < .05). The iPTA was more sensitive than conventional LTA test in detecting susceptibility of patient cells to in vitro toxicity (P < .05). The novel iPTA has considerable potential as an investigative tool for DHS as it is more sensitive and cheaper, requiring no special reagents.

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)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.413
Teacher spread0.344 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations16
Published2013
Admission routes1
Has abstractyes

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