Effectiveness of GH isoform differential immunoassay for detecting rhGH doping on application of various growth factors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".