The Effect of Sports Injury on Insulin-Like Growth Factor-I and Type 3 Procollagen: Implications for Detection of Growth Hormone Abuse in Athletes
Bibliographic record
Abstract
CONTEXT: A method to detect exogenously administered growth hormone (GH) based on the measurement of two GH-dependent markers, IGF-I and type 3 procollagen (P-III-P) has been proposed. Skeletal or soft tissue injury may alter these markers. Elevations in either of these proteins after injury might lead to a false accusation of doping with GH. OBJECTIVE: The objective of the study was to assess the effect of musculoskeletal or soft tissue injury on IGF-I and P-III-P concentrations in amateur and elite athletes and assess the effect of injury on the proposed GH detection method. DESIGN: This was a longitudinal observational study after sporting injury. SETTING: The study was conducted at Southampton General Hospital and British Olympic Medical Centre. SUBJECTS: Subjects included elite and amateur athletes after an injury. INTERVENTION: Interventions included measurement of IGF-I and P-III-P and application of the GH-2000 discriminant function score up to 84 d after an injury as well as classification of injury by type and severity. OUTCOME MEASURES: IGF-I and P-III-P concentration and ability to detect GH abuse in athletes without the risk of false accusation because of an injury were measured. RESULTS: There was no change in IGF-I concentration after an injury. By contrast, P-III-P concentrations rose by 41.1 +/- 16.6%, reaching a peak around 14 d after an injury. The rise in P-III-P varied according to injury type and severity. This rise had a trivial effect on the GH-2000 discriminant function score, and no subject reached the threshold needed for a doping offense. CONCLUSIONS: Although there was a rise in P-III-P after injury, this was insufficient to invalidate the GH-2000 detection method based on IGF-I and P-III-P concentrations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".