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Record W752724420 · doi:10.1177/1087057115594590

Fit-for-Purpose Radio Receptor Assay for the Determination of Growth Hormone Secretagogues in Urine

2015· article· en· W752724420 on OpenAlexfundno aff
P. Ferro, Ricardo Gutiérrez–Gallego, Jaume Bosch, Magı́ Farré, Jordi Segura

Bibliographic record

VenueSLAS DISCOVERY · 2015
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsnot available
FundersMinisterio de Ciencia e InnovaciónUniversidade de Santiago de CompostelaWorld Anti-Doping Agency
KeywordsUrineGrowth hormoneHormoneAthletesReceptorChromatographyProtocol (science)ChemistryEndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

The everlasting pharmacological development is continuously producing new substances with potential doping abuse. Among these, secretagogues are very prone to misuse by athletes for their properties to release growth hormone (GH) and some limitations in the actual analytical methods to detect them. In this paper, an in-depth study on the key variables of the radio receptor method previously developed by our group is performed and a fit-for-purpose protocol is established. Thus, this sensitive and robust screening method is proposed as an intelligent and preventive antidoping method to detect new growth hormone secretagogues (GHSs) in exceptional suspicious urine samples obtained from athletes and will support the current detection methods based on liquid chromatography-mass spectrometry (LC-MS).

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)0.002
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.043
GPT teacher head0.304
Teacher spread0.261 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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Same venueSLAS DISCOVERYSame topicAdipose Tissue and MetabolismFrench-language works237,207