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Record W1877767327 · doi:10.18433/j3fk5r

Pharmacokinetic Study of Modafinil in Relation to Gender and Ethnicity in Healthy Young Chinese Volunteers

2010· article· en· W1877767327 on OpenAlexvenueno aff
Tao Guo, Longshan Zhao, Xia Dong-ya

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsModafinilPharmacokineticsVolume of distributionMedicineMetabolitePlasma concentrationPharmacologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Pharmacokinetics of modafinil were investigated in relation to sex and ethnicity in healthy young volunteers from Han, Mongolian, Korean, Uygur and Hui (n = 10/group) following administration of a single 200 mg oral dose. METHODS: Blood samples were collected over 48 h for the determination of plasma levels of modafinil and its acid metabolite by HPLC. Pharmacokinetic parameters were evaluated using noncompartmental methods. RESULTS: Modafinil was well tolerated and safe at a single oral dose of 200 mg. All participants reported adverse events, none of which was serious or unexpected. The maximum plasma concentration (C(max)) and area under the curve for modafinil concentration versus time, which was extrapolated to infinity (AUC(0-∞)), were higher in women compared to men (p < 0.01). No sex-based difference was noted in the total body weight-normalized modafinil oral clearance. The total body weight-normalized modafinil apparent volume of distribution and t1/2 were found to exhibit ethnicity-based significant differences. CONCLUSION: There are pharmacokinetic differences based on sex and ethnicity for modafinil.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.123
GPT teacher head0.497
Teacher spread0.374 · 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 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

Citations6
Published2010
Admission routes1
Has abstractyes

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