Statistical significance of hair analysis of clenbuterol to discriminate therapeutic use from contamination
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
Clenbuterol is a well-established β2-agonist, which is prohibited in sports and strictly regulated for use in the livestock industry. During the last few years clenbuterol-positive results in doping controls and in samples from residents or travellers from a high-risk country were suspected to be related the illegal use of clenbuterol for fattening. A sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to detect low clenbuterol residues in hair with a detection limit of 0.02 pg/mg. A sub-therapeutic application study and a field study with volunteers, who have a high risk of contamination, were performed. For the application study, a total dosage of 30 µg clenbuterol was applied to 20 healthy volunteers on 5 subsequent days. One month after the beginning of the application, clenbuterol was detected in the proximal hair segment (0-1 cm) in concentrations between 0.43 and 4.76 pg/mg. For the second part, samples of 66 Mexican soccer players were analyzed. In 89% of these volunteers, clenbuterol was detectable in their hair at concentrations between 0.02 and 1.90 pg/mg. A comparison of both parts showed no statistical difference between sub-therapeutic application and contamination. In contrast, discrimination to a typical abuse of clenbuterol is apparently possible. Due to these findings results of real doping control samples can be evaluated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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