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Record W1906566966 · doi:10.1002/dta.1423

Biomarker detection of rhGH Doping: an excretion study

2012· article· en· W1906566966 on OpenAlexfundno aff
Jing Jing, Xinmiao Zhou, Chun‐Ting He, Lisi Zhang, Sheng Yang, Youxuan Xu, Minhao Xie, Yi Yan, Hao Su, Moutian Wu

Bibliographic record

VenueDrug Testing and Analysis · 2012
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsBiomarkerMedicineInternal medicineGrowth hormoneEndocrinologyExcretionHuman growth hormoneHormoneChemistry

Abstract

fetched live from OpenAlex

The purpose of this research is to validate the biomarker-based approach for the detection of doping with recombinant human growth hormone (rhGH) in sport. The GH-2000 project proposed an indirect method for the detection of exogenously administered growth hormone (GH) based on the measurement of the GH-dependent markers: insulin-like growth factor-I (IGF-I) and Type III pro-collagen (P-III-P). These markers rise in a dose-dependent manner after GH application. In this study, the concentrations of IGF-I, IGF-BP3, and P-III-P in serum were determined to provide further incentives for the implementation of this detection assay in modern anti-doping programmes. This paper reports on an administration study of rhGH involving 25 Chinese male volunteers at a dose of 0.1 IU /kg/day for a continuous 14-day period. We observed that the serum IGF-I concentration increased rapidly in the rhGH treatment group and showed significantly higher levels compared to baseline between days 4 and day 16 after administration. Although the response of P-III-P to rhGH administration was delayed compared to the IGF-I axis, the P-III-P concentration remained increased for a longer period (from day 4 to day 28). Statistical analysis was carried out to establish a discriminant formula with Statistical Product and Service Solutions (SPSS) concluding that the biomarker methodology is valid and universally applicable.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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