Application of the Athlete Biological Passport Approach to the Detection of Growth Hormone Doping
Bibliographic record
Abstract
CONTEXT: Because of its anabolic and lipolytic properties, growth hormone (GH) use is prohibited in sport. Two methods based on population-derived decision limits are currently used to detect human GH (hGH) abuse: the hGH Biomarkers Test and the Isoforms Differential Immunoassay. OBJECTIVE: We tested the hypothesis that longitudinal profiling of hGH biomarkers through application of the Athlete Biological Passport (ABP) has the potential to flag hGH abuse. METHODS: Insulin-like growth factor 1 (IGF-1) and procollagen III peptide (P-III-NP) distributions were obtained from 7 years of anti-doping data in elite athletes (n = 11 455) and applied as priors to analyze individual profiles from an hGH administration study in recreational athletes (n = 35). An open-label, randomized, single-site, placebo-controlled administration study was carried out with individuals randomly assigned to 4 arms: placebo, or 3 different doses of recombinant hGH. Serum samples were analyzed for IGF-1, P-III-NP, and hGH isoforms and the performance of a longitudinal, ABP-based approach was evaluated. RESULTS: An ABP-based approach set at a 99% specificity level flagged 20/27 individuals receiving hGH treatment, including 17/27 individuals after cessation of the treatment. ABP sensitivity ranged from 12.5% to 71.4% across the hGH concentrations tested following 7 days of treatment, peaking at 57.1% to 100% after 21 days of treatment, and was maintained between 37.5% and 71.4% for the low and high dose groups 1 week after cessation of treatment. CONCLUSION: These findings demonstrate that longitudinal profiling of hGH biomarkers can provide suitable performance characteristics for use in anti-doping programs.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".