Serum Insulin-Like Growth Factor-I and Pro-Collagen Type III N-Terminal Peptide in Adolescent Elite Athletes: Implications for the Detection of Growth Hormone Abuse in Sport
Bibliographic record
Abstract
CONTEXT: A method based on two GH-dependent markers, IGF-I and pro-collagen type III N-terminal peptide (P-III-P), has been devised to detect exogenously administered GH. Because previous studies on the detection of GH abuse involved predominantly adult athletes, the method must be validated in adolescent athletes. OBJECTIVE: The aim of the study was to examine serum IGF-I and P-III-P concentrations in elite adolescent athletes and to determine whether the method developed in adults is appropriate to detect GH abuse in this population. DESIGN AND SETTING: We conducted a cross-sectional observational study at national sporting organization training events. SUBJECTS: A total of 157 (85 males, 72 females) elite athletes between 12 and 20 yr of age participated in the study. INTERVENTION: Serum IGF-I and P-III-P were each measured by two commercially available immunoassays. GH-2000 discriminant function scores were calculated. RESULTS: Both IGF-I and P-III-P rose to a peak during adolescence, which was earlier in girls than in boys. All GH-2000 scores lay below the proposed cutoff limit of 3.7 (although some scores were close to this value), indicating that none of these athletes would be accused of GH doping if the GH-2000 discriminant formulae were used. The results between the two immunoassays for IGF-I and P-III-P were closely aligned. CONCLUSIONS: The GH-2000 score rises in early adolescence, reaches a peak in athletes aged 13-16 yr, and then falls. We have found no evidence that the proposed GH-2000 score developed in adults would lead to an unacceptable rate of false-positive results in adolescent athletes, but caution may be required around the time of peak growth velocity.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".