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Record W2140889316 · doi:10.1093/jat/34.4.210

Analysis of Stimulants in Oral Fluid and Urine by Gas Chromatography-Mass Spectrometry II: Pseudophedrine

2010· article· en· W2140889316 on OpenAlexfundno aff
Sabina Strano Rossi, Daniela Leone, Xavier de la Torre, Francesco Botrè

Bibliographic record

VenueJournal of Analytical Toxicology · 2010
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsnot available
FundersMinistero della SaluteWorld Anti-Doping Agency
KeywordsEphedrinePseudoephedrineUrineChromatographyChemistryNasal decongestantOral administrationPharmacologyMedicineAnesthesia

Abstract

fetched live from OpenAlex

This study was designed to optimize a method for the identification and quantification of ephedrines in oral fluid (OF) and for its application to subjects taking different doses of pseudoephedrine. Ephedrines use by athletes is banned by World Anti-Doping Agency (WADA), only "in competition" if their concentration in urine exceeds the cutoff limit. The study aimed to establish if there is a correlation in terms of times of elimination and of concentration trends of ephedrine in OF and urine after administration of therapeutic doses of pseudoephedrine to various subjects. Results obtained from excretion studies performed on eight subjects showed reproducible times of disappearance of ephedrines from OF. Pseudoephedrine was generally at low concentrations or undetectable in oral fluid samples 12 h after administration, whereas urine samples collected in the same period of time showed higher ephedrine concentrations and exceeding cutoff values generally between 8 and 24 h after administration of the drug. Within- and between-individual variability was observed in terms of concentrations of pseudoephedrine in OF following the administration of the same dose. Only in the case of sustained-release drugs were constant pseudoephedrine concentrations achieved in OF.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.322
Teacher spread0.310 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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