The use of growth hormone (GH)‐dependent markers in the detection of GH abuse in sport: Physiological intra‐individual variation of IGF‐I, type 3 pro‐collagen (P‐III‐P) and the GH‐2000 detection score
Bibliographic record
Abstract
BACKGROUND: Growth Hormone is abused by athletes for its lipolytic and anabolic properties. Its use is prohibited by the World Anti-Doping Agency. The GH-2000 project developed a methodology to detect its abuse using the concentrations of two GH-dependent biomarkers, IGF-I and type 3 procollagen (P-III-P). The sensitivity of this method may be improved by considering intra-individual variability. AIM: The aim of this study was to examine the intra-individual variability of IGF-I, P-III-P and the GH-2000 score. SUBJECTS AND METHODS: IGF-I, P-III-P and GH-2000 score were evaluated in four longitudinal studies involving 303 elite and 78 amateur athletes. Samples were collected over a period of up to 12 months from a total of 238 men and 143 women aged between 17 and 53 years (mean 24.2). RESULTS: The four studies showed good agreement with no apparent difference in within-individual variation between amateur and elite athletes. The intra-individual variability for IGF-I ranged between 14-16% while the variability for P-III-P was 7-18%. No athlete tested positive for growth hormone during any of the studies. The overall mean intra-individual variability of the GH-2000 score was less than 0.6 units in all studies. CONCLUSIONS: The high stability of marker levels suggests that concentrations are largely genetically determined. Adopting a test based on the concept of an athlete's 'passport' or 'profiling' would take advantage of this and most likely increase the sensitivity of the test. These data also provide strong evidence that a positive test result for GH abuse would not occur as a result of chance variability.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".