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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it