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Evaluating the predictive value of the in vitro Platelet Toxicity Assay (iPTA) for the diagnosis of hypersensitivity reactions to sulfonamide drugs: A prospective case‐control study.

2013· article· en· W3167580773 on OpenAlexaff
Abdelbaset A Elzzagallaai, Gideon Koren, Michael Rieder

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWestern University
Fundersnot available
KeywordsToxicityPredictive valueDrugMedicineIn vitroPlateletPharmacologyImmunologyInternal medicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Drug hypersensitivity reactions (DHRs) are rare but potentially fatal types of adverse drug reactions that develop in susceptible patients following exposure to certain drugs including sulfonamides. Their diagnosis is challenging due to lack of safe and reliable test. The aim of this study was to evaluate the predictive value of the in vitro Platelet Toxicity Assay (iPTA) in diagnosis of DHRs to sulfonamides and to compare its performance to the conventional lymphocyte Toxicity Assay (LTA) test. Blood samples were obtained from 66 individuals (36 DHS‐sulfa patients and 30 controls) and LTA and iPTA were performed. Results were then analyzed to estimate the predictive value, sensitivity and agreement of the two tests. The concentration‐dependent toxicity was significantly greater in the cells from patients versus controls (p<0.05). The two tests had a high degree of agreement (correlation coefficient: R2 = 0.97). The iPTA was significantly more sensitive than the conventional LTA test in detecting the susceptibility of patient cells to in vitro toxicity (p<0.05). The iPTA has considerable potential as a diagnostic tool for DHS as it is cheaper to perform, requires no special reagents and has a greater sensitivity in detecting patients predisposed to DHRs to sulfonamides.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.630
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.324
Teacher spread0.288 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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