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Record W2135996866 · doi:10.1002/dta.392

Studies of methylhexaneamine in supplements and geranium oil

2011· article· en· W2135996866 on OpenAlexfundno aff
Antonino De Lisi, Nicole Hasick, Romas J. Kazlauskas, Catrin Goebel

Bibliographic record

VenueDrug Testing and Analysis · 2011
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersDepartment of the Prime Minister and CabinetWorld Anti-Doping AgencyAustralian Government
KeywordsGeraniumPhenethylaminesCaffeineMedicineTraditional medicinePharmacologyChemistryBiologyBotanyPsychiatry

Abstract

fetched live from OpenAlex

A number of supplements are now available which are sold as fat burners or pre-workout boosters and contain stimulants which are banned in sport. Many contain methylhexaneamine under one of many pseudonyms including Geranamine, geranium oil or extract, or a number of chemical names such as 1,3-dimethylpentylamine. This has resulted in many athletes returning an adverse finding and having sanctions imposed. Other stimulants such as caffeine, phenpromethamine, synefrine, and phenethylamines are also to be found in supplements. This communication shows that geranium oils do not contain methylhexaneamine and that products labelled as containing geranium oil but which contain methylhexaneamine can only arise from the addition of synthetic material. Since the usual dose of methylhexaneamine is large, the drug is excreted at relatively high amounts for more than 29 h, the time for which the excretion was studied.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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 designBench or experimental
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

Citations47
Published2011
Admission routes1
Has abstractyes

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