Influence of ethnicity on IGF‐I and procollagen III peptide (P‐III‐P) in elite athletes and its effect on the ability to detect GH abuse
Bibliographic record
Abstract
CONTEXT: A method based on the two GH dependent markers, IGF-I and procollagen III peptide (P-III-P) has been proposed to detect exogenously administered GH. As previous studies involved predominantly white European elite athletes, it is necessary to validate the method in other ethnic groups. OBJECTIVE: To examine serum IGF-I and P-III-P in elite athletes of different ethnicities within 2 h of competing at national or international events. DESIGN: Cross-sectional observational study. SETTING: National and International sporting events. SUBJECTS: 1085 elite athletes of different ethnicities. INTERVENTION: Serum IGF-I and P-III-P were measured and GH-2000 discriminant function score was calculated. Effect of ethnicity was assessed. RESULTS: In men, IGF-I was 21.7 +/- 2.6% lower in Afro-Caribbeans than white Europeans (P < 0.0001) but there were no differences between other ethnic groups. In women, IGF-I was 14.2 +/- 5.1% lower in Afro-Caribbeans (P = 0.005) and 15.6 +/- 7.0% higher in Orientals (P = 0.02) compared with white Europeans. P-III-P was 15.2 +/- 3.5%, 26.6 +/- 6.6% and 19.3 +/- 5.8% lower in Afro-Caribbean (P < 0.0001), Indo-Asian (P < 0.0001) and Oriental men (P = 0.001), respectively, compared with white European men. In women, P-III-P was 15.7 +/- 4.7% lower in Afro-Caribbeans compared to white Europeans (P =0.0009) but there were no differences between other ethnicities. Despite these differences, most observations were below the upper 99% prediction limits derived from white European athletes. All GH-2000 scores lay below the cut-off limit proposed for doping. CONCLUSIONS: The GH-2000 detection method based on IGF-I and P-III-P would be valid in all ethnic groups.
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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 | 0.000 | 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".