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Record W2135163551 · doi:10.1542/peds.2009-1907

Fatal Hydrocodone Overdose in a Child: Pharmacogenetics and Drug Interactions

2010· article· en· W2135163551 on OpenAlexaff
Parvaz Madadi, Doris Hildebrandt, Inna Y. Gong, Ute I. Schwarz, Catherine Ciszkowski, Colin J.D. Ross, Johanna Sistonen, Bruce Carleton, Michael R. Hayden, Albert E. Lauwers, Gideon Koren

Bibliographic record

VenuePEDIATRICS · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaHospital for Sick ChildrenUniversity of British ColumbiaChild and Family Research InstituteWestern UniversityOffice of the Chief Medical Examiner
Fundersnot available
KeywordsHydrocodoneMedicineCYP2D6PharmacogeneticsHydromorphoneDrugPharmacologyDrug interactionMetaboliteAnesthesiaOpioidCytochrome P450Internal medicineOxycodoneGenotype

Abstract

fetched live from OpenAlex

Fatal opioid toxicity occurred in a developmentally delayed child aged 5 years 9 months who was inadvertently administered high doses of hydrocodone for a respiratory tract infection. The concentration of hydrocodone in postmortem blood was in the range associated with fatality; however, hydromorphone, a major metabolite catalyzed by cytochrome P450 2D6 (CYP2D6), was not detected when using mass spectrometry. Genetic analysis revealed that the child had a reduced capability to metabolize the drug via the CYP2D6 pathway (CYP2D6*2A/*41). Coadministration of clarithromycin (a potent cytochrome P450 3A4 inhibitor) for an ear infection and valproic acid for seizures since birth further prevented drug elimination from the body. This case highlights the interplay between pharmacogenetic factors, drug-drug interactions, and dose-related toxicity in a child.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.402
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations94
Published2010
Admission routes1
Has abstractyes

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Same venuePEDIATRICSSame topicPharmacogenetics and Drug MetabolismFrench-language works237,207