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Record W2105874372 · doi:10.1517/17425250902800153

Ethnic differences in drug metabolism and toxicity from chemotherapy

2009· review· en· W2105874372 on OpenAlexaff
Viet Hong Phan, Melissa M. Moore, Andrew J. McLachlan, Micheline Piquette‐Miller, Hongmei Xu, Stephen Clarke

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2009
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPharmacokineticsIrinotecanDrugPharmacologyDrug metabolismToxicityPharmacogeneticsPaclitaxelMedicinePharmacodynamicsBiologyChemotherapyCancerInternal medicineGeneticsGenotypeGene

Abstract

fetched live from OpenAlex

There is wide inter-individual variability in the pharmacokinetics, pharmacodynamics and tolerance of anticancer drugs. Recent evidence suggests that there is even greater variability between individuals of different ethnicity. Allelic variants of genes encoding drug metabolising enzymes are expressed with different incidences in different ethnic groups, particularly between Asian and Caucasians, and some of these variants result in altered enzyme function. There is also preliminary evidence to suggest that ethnic differences in the expression of allelic variants may produce altered pharmacokinetics of anticancer drugs, including paclitaxel and irinotecan. Emerging evidence indicates that toxicity from certain anticancer treatments is much greater in Asian patients than Caucasians in breast and lung cancers. Understanding the causes of ethnic differences in cytotoxic metabolism may promote improved understanding of inter-individual differences in the pharmacokinetics and tolerance of cytotoxic drugs leading to improved and more individualised prescribing.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.177
GPT teacher head0.468
Teacher spread0.291 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations121
Published2009
Admission routes1
Has abstractyes

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