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Record W2099744961 · doi:10.1177/00912700122010663

Population Pharmacokinetics of Fentanyl in Healthy Volunteers

2001· article· en· W2099744961 on OpenAlexaff
Robert E. Ariano, Peter C. Duke, D. S. Sitar

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2001
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsPharmacokineticsFentanylVolume of distributionPopulation pharmacokineticsPopulationMedicineDistribution (mathematics)AnesthesiaNonparametric statisticsPharmacologyMathematicsStatistics

Abstract

fetched live from OpenAlex

The authors compared the population pharmacokinetics of fentanyl using a standard individualized modeling (SIM) approach versus that of a nonparametric expectation maximization (NPEM) approach. The pharmacokinetic properties of fentanyl administered as a single 5 ug/kg intravenous infusion were evaluated in 18 healthy volunteers by use of SIM as well as with NPEM. NPEM-derived parameters were a total body clearance of 2.12 +/- 0.28 L/kg/h, distributional clearance of 8.43 +/- 4.58 L/kg/h, central volume of distribution of 1.22 +/- 0.21 L/kg, and peripheral volume of distribution of 1.81 +/- 1.47 L/kg. Identified parameter values from the modeling methods resulted in virtually identical simulated profiles; this finding was confirmed when median values noted were not statistically significantly different between modeling methods (SIM or NPEM). However, the NPEM algorithm uniquely identified a greater distributional clearance in the elderly population and also illustrated a profile with at least 10% of the study population having a very high clearance of fentanyl. This finding may affect the therapeutic use of fentanyl. NPEM allows for a more informative global representation of a drug's pharmacokinetics.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.233
GPT teacher head0.561
Teacher spread0.328 · 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.

Study designObservational
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

Citations30
Published2001
Admission routes1
Has abstractyes

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