Growth hormone, IGF‐I and insulin and their abuse in sport
Bibliographic record
Abstract
There is widespread anecdotal evidence that growth hormone (GH) is used by athletes for its anabolic and lipolytic properties. Although there is little evidence that GH improves performance in young healthy adults, randomized controlled studies carried out so far are inadequately designed to demonstrate this, not least because GH is often abused in combination with anabolic steroids and insulin. Some of the anabolic actions of GH are mediated through the generation of insulin-like growth factor-I (IGF-I), and it is believed that this is also being abused. Athletes are exposing themselves to potential harm by self-administering large doses of GH, IGF-I and insulin. The effects of excess GH are exemplified by acromegaly. IGF-I may mediate and cause some of these changes, but in addition, IGF-I may lead to profound hypoglycaemia, as indeed can insulin. Although GH is on the World Anti-doping Agency list of banned substances, the detection of abuse with GH is challenging. Two approaches have been developed to detect GH abuse. The first is based on an assessment of the effect of exogenous recombinant human GH on pituitary GH isoforms and the second is based on the measurement of markers of GH action. As a result, GH abuse can be detected with reasonable sensitivity and specificity. Testing for IGF-I and insulin is in its infancy, but the measurement of markers of GH action may also detect IGF-I usage, while urine mass spectroscopy has begun to identify the use of insulin analogues.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".