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Record W2022326153 · doi:10.1177/0091270003261496

Effect of St. John's Wort on the Pharmacokinetics of Theophylline in Healthy Volunteers

2003· article· en· W2022326153 on OpenAlexaboutno aff
Takuya Morimoto, Tsutomu Kotegawa, Kimiko Tsutsumi, Yasukiyo Ohtani, Hiromitsu Imai, Shigeyuki Nakano

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTheophyllinePharmacokineticsUrineCrossover studyHypericum perforatumPharmacologyMedicineOral administrationPlasma concentrationInternal medicine

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the effect of St. John's wort (SJW, Hypericum perforatum) on the pharmacokinetics of theophylline in healthy volunteers. Twelve healthy Japanese male volunteers participated in this randomized, open-labeled, crossover study. The subjects took an SJW caplet (300 mg) three times a day for 15 days. On day 14, they received a single oral dose of 400 mg of theophylline. They took the same dose of theophylline without SJW treatment on another occasion. Plasma and urine samples were obtained during a 48-hour period after theophylline administration. Theophylline concentrations in plasma and urine, as well as the major metabolites (13U, 1U, 3X) in urine, were measured. SJW caused no significant changes in the pharmacokinetics of theophylline in plasma. SJW administration tended to increase the ratio of 1U/the total amount excreted in urine. However, no changes in the ratio of unchanged theophylline, 13U, and 3X were observed. It is unlikely that the effect of 15 days of treatment with SJW on CYPs is sufficient to cause a change in plasma theophylline concentrations.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.405
Teacher spread0.352 · 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 designNon-randomized trial
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

Citations62
Published2003
Admission routes1
Has abstractyes

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