Longitudinally monitoring of P‐III‐NP, IGF‐I, and GH‐2000 score increases the probability of detecting two weeks’ administration of low‐dose recombinant growth hormone compared to GH‐2000 decision limit and GH isoform test and micro RNA markers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To detect doping with growth hormone (GH), GH isoform and biomarkers tests are available. Both methods use population-based decision limits. Future testing in anti-doping is progressing toward individual-based reference ranges, and it is possible that with such an approach the sensitivity to detect GH doping may increase. In addition to monitoring different proteins, the use of miRNAs as future GH biomarkers has been discussed. Here we have longitudinally studied the serum concentrations of IGF-I, P-III-NP and the different GH isoforms in nine healthy men prior to, during and after two weeks' administration with low doses (1 and 4 IU/day) of recGH. Moreover, three putative miRNAs were analyzed. The results show that 80% of the participants were identified as atypical findings using the GH isoform test. However, the participants were only positive 1.5-3 hours directly after an injection. Only one of the participants reached a GH-2000 score indicative of doping when a population-based decision limit was applied. When IGF-I and P-III-NP were longitudinally monitored, 88% of the participants were identified above an individual upper threshold arbitrarily calculated as three standard deviations above the mean values of four baseline samples. The miRNA levels displayed large intra-subject variations that did not change in relation to recGH administration. Our results show that the GH isoform test is very sensitive in detecting low doses of recGH but with a short detection window. Moreover, longitudinally monitoring of IGF-I and P-III-NP may be a promising future approach to detect GH doping.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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