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Record W4390799616 · doi:10.1002/9781119660699.ch15

Pharmacokinetic Behaviors of Orally Administered Drugs

2023· other· en· W4390799616 on OpenAlexaff
Hamdah Al Nebaihi, Dion R. Brocks, Jaime A. Yáñez, M. Laird Forrest, Neal M. Davies

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPharmacokineticsPharmacologyDispositionOral administrationDrugAbsorption (acoustics)Distribution (mathematics)MedicinePhysiologically based pharmacokinetic modellingChemistryPsychologyMathematicsMaterials science

Abstract

fetched live from OpenAlex

This chapter introduces the reader to some basic principles of oral pharmacokinetics and provides background knowledge of the pharmacokinetic parameters involved after oral administration. It explains the different physicochemical and physiological factors that affect oral disposition of drugs. Pharmacokinetics (PK) utilizes mathematical models and equations to describe, understand, and predict the rate processes of absorption, distribution, metabolism, and elimination using concentration–time data obtained by experimentation. A PK model can be described as a mathematical approach to characterize the concentration–time profile of drugs. The chapter provides a comprehensive, rather than exhaustive, appraisal of pharmacokinetic behaviors of orally administered drugs. Pregnant women are susceptible to illnesses and/or pregnancy-induced complications that might require a drug regime. The coingestion of certain fruit juices are known to be associated with either changes in metabolism or transport of drugs, thus impeding or enhancing the extent of drug absorption.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.004

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 head0.132
GPT teacher head0.458
Teacher spread0.326 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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