A robust test for growth hormone doping – present status and future prospects
Bibliographic record
Abstract
Although doping with growth hormone (GH) is banned, there is anecdotal evidence that it is widely abused. GH is reportedly used often in combination with anabolic steroids at high doses for several months. Development of a robust test for GH has been challenging because recombinant human 22 kDa (22K) GH used in doping is indistinguishable analytically from endogenous GH and there are wide physiological fluctuations in circulating GH concentrations. One approach to GH testing is based on measurement of different circulating GH isoforms using immunoassays that differentiate between 22K and other GH isoforms. Administration of 22K GH results in a change in its abundance relative to other endogenous pituitary GH isoforms. The differential isoform method has been implemented; however, its utility is limited because of the short window of opportunity of detection. The second approach, which will extend the window of opportunity of detection, is based on the detection of increased levels of circulating GH-responsive proteins, such as insulin-like growth factor (IGF) axis and collagen peptides. Age and gender are the major determinants of variability for IGF-I and the collagen markers; therefore, a test based on these markers must take age into account for men and women. Extensive data is now available that validates the GH-responsive marker approach and implementation is now largely dependent on establishing an assured supply of standardized assays. Future directions will include more widespread implementation of both approaches by the World Anti-Doping Agency, possible use of other platforms for measurement and an athlete's passport to establish individual reference levels for biological parameters such as GH-responsive markers. Novel approaches include gene expression and proteomic profiling.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".