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Record W1551973683 · doi:10.1002/dta.1385

Effectiveness of GH isoform differential immunoassay for detecting rhGH doping on application of various growth factors

2012· article· en· W1551973683 on OpenAlexfundno aff
Masato Okano, Yasunori Nishitani, Mitsuhiko Sato, Shinji Kageyama

Bibliographic record

VenueDrug Testing and Analysis · 2012
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsImmunoassayEndocrinologyInternal medicineGene isoformMedicineGrowth hormoneHormoneAntibodyChemistryImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The analytical method for detecting growth hormone (GH) doping, the so-called GH isoform differential immunoassay, is currently approved by the World Anti-Doping Agency (WADA). Anti-doping laboratories often face challenges by athletes' lawyers and need to have various types of scientific evidence against the claim that the adverse analytical finding (AAF) result was caused by excess ectopic or abnormal excretion. In this work, a population study of Japanese athletes (255 male and 256 female) and administration studies of recombinant human GH (rhGH) in Japanese females were conducted to confirm the applicability of GH isoform differential immunoassay. The present paper describes the effectiveness of the GH isoform differential immunoassay under abnormal excretion of endogenous GH as determined by administration studies of GH releasing hormone (GHRH(1-44)) and insulin-like growth factor-1 (IGF-1). No false positive findings were found in Japanese athletes. The GH isoform differential immunoassays could detect application of rhGH for approximately 12-24 h. The administration of GHRH(1-44) and IGF-1 as well as ghrelin receptor agonists did not affect the isoform ratio (no false positives). We conclude that the GH isoform differential immunoassay is a highly specific method for detecting rhGH doping. Subject-based profiling (i.e. athlete biological passport) very likely will represent a highly sensitive approach for detecting rhGH doping.

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)0.001
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 score0.068
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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