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Record W2233372425 · doi:10.1002/dta.1933

Analysis of tolvaptan and its metabolites in sports drug testing by high‐performance liquid chromatography coupled to tandem mass spectrometry

2016· article· en· W2233372425 on OpenAlexfundno aff
Sebastian Rzeppa, L. N. Viet

Bibliographic record

VenueDrug Testing and Analysis · 2016
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsnot available
FundersOtsuka PharmaceuticalWorld Anti-Doping Agency
KeywordsChromatographyChemistryTolvaptanMetaboliteUrineTandem mass spectrometryHigh-performance liquid chromatographyLiquid chromatography–mass spectrometryMass spectrometryPharmacologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Tolvaptan is prohibited by the World Anti-doping Agency (WADA) under class S5 - Diuretics and masking agents. Less than 1% of the administrated dose is excreted by humans in urine. Knowledge concerning the metabolism in humans, and especially the excretion of metabolites in human urine, is limited. An analysis method based on the dilute-and-shoot approach using high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS) for detection was developed and validated. Ion transitions, which are part of this method, can easily be included in already existing screening methods used in routine doping analysis for the detection of diuretics. After administration of a single dose of tolvaptan to one male subject, low concentrations of the drug itself could be detected in urine samples over a time period of 24 h. In addition, hydroxyl metabolites of tolvaptan and one carboxyl metabolite with a cleaved benzazepine ring system were identified. These metabolites showed detection times of up to 150 h. An inclusion of these metabolites in the methods used in doping control analysis seems therefore to be of value. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.242
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.232
Teacher spread0.223 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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