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Genotypic Approaches to Therapy in Children (GATC): Using Information Technology to Improve Drug Safety

2009· article· en· W107665127 on OpenAlexaffabout
Elaine Wong, Bruce Carleton, Daniel F. B. Wright, Mark A. Smith, L Verbeek, Celia Hildebrand, P Stannard, Régis Vaillancourt, P Elliot-Miller, Colin J.D. Ross, Michael R. Hayden

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsDrug reactionMedicineAdverse drug reactionDrugAdverse effectGenetic diagnosisBioinformaticsPediatricsIntensive care medicineInternal medicinePharmacologyGeneticsGeneBiology

Abstract

fetched live from OpenAlex

Adverse drug reactions (ADRs) are a major cause of morbidity and mortality in children. Current models of ADR surveillance have repeatedly demonstrated little pragmatic value to practicing clinicians. ADR reporting rates in the US and Canada suggest that only 5% of ADRs are reported. The Genotypic Approaches to Therapy in Children (GATC) network was established to identify and solve drug safety problems in paediatrics. We hypothesized that genetic polymorphisms underlie a significant portion of concentration-dependent ADRs in children. Our objective was to establish an ADR active surveillance network in paediatric hospitals across Canada. Surveillance clinicians evaluate clinical information from ADR cases and drug-matched controls, and collected DNA samples from all patients. The surveillance network will enable the identification of predictive genomic-markers for ADRs. With this knowledge, children at risk can be identified before therapy is initiated and enable personalized adjustments to therapy based on genetic make-up.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.113
GPT teacher head0.410
Teacher spread0.297 · 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 designOther design
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

Citations1
Published2009
Admission routes2
Has abstractyes

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