La Hormona de crecimiento en el ámbito deportivo : métodos de detección, comparación y funcionamiento metodológico
Bibliographic record
Abstract
This doctoral thesis describes the evaluation of analytical methodologies to detect human growth hormone (hGH) abuse in sport. Two main strategies are studied and compared. The first (direct method) measures changes in the proportions of hGH variants, expressed as ratios, which are altered after hGH administration. Two versions of the direct method are compared; one using ratios between recombinant and pituitary variants and a second using ratios between only 22 and 20 kDa hGH variants. To fully understand the immunoassays readings, all relevant antibodies were characterised by surface plasmon resonance (SPR). A second strategy (indirect method) measures changes in proteins, other than hGH, provoked by the use of hGH. These bio-markers should have a longer retrospective analytical power. To evaluate the performance of both strategies, two clinical trials with recombinant hGH (rhGH) were carried out with healthy male subjects. The resulting data have been compared and statistically assessed. It is the first time that all available strategies have been applied on a single data set and this allows better understanding of the analytical readings which provides an additional tool in the evaluation of anti-doping analyses.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".