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Record W4389096964 · doi:10.1002/dta.3615

Evaluation of sulfate metabolites as markers of topical testosterone administration in Caucasian and Asian populations

2023· article· en· W4389096964 on OpenAlexfundno aff
Claudia Bressan, Élida Alechaga, Núria Monfort, Rosa Ventura

Bibliographic record

VenueDrug Testing and Analysis · 2023
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsTestosterone (patch)SulfatePharmacologyMedicineInternal medicineTraditional medicinePhysiologyChemistry

Abstract

fetched live from OpenAlex

Sulfate metabolites of endogenous anabolic androgenic steroids (EAAS) have been shown to prolong the detection times compared with the conventional urinary markers of the steroid profile for oral and intramuscular administrations of testosterone (T). In this work, the sensitivity of sulfate EAAS markers for the detection of T gel administration has been evaluated in six Caucasian and six Asian male volunteers. Fourteen sulfate metabolites were measured in basal and post-administration samples after multiple doses of T gel (100 mg/day, three consecutive days), and the detection times based on individual thresholds for each volunteer were evaluated. Sulfate concentrations did not show adequate sensitivity, but the results of sulfate ratios were much more promising. Androsterone sulfate/testosterone sulfate (A-S/T-S), epiandrosterone sulfate/epitestosterone sulfate (epiA-S/E-S), epiA-S/T-S, and etiocholanolone sulfate/epitestosterone sulfate (Etio-S/E-S) provided the most consistent detectability for all volunteers and populations, with detection times ranging from 60 to 96 h since the first dose. Additional ratios improved detectability to up to 7 days, but only in particular volunteers. In general, sensitivity was similar to or better than the conventional testosterone/epitestosterone ratio (T/E) of the steroid profile, which further reinforces the conclusion that sulfate EAAS metabolites can be a good complement for the current steroid profile.

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)0.002
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 score0.024
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.081
GPT teacher head0.356
Teacher spread0.276 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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