Identification of hormone esters in injection site in muscle tissues by LC/MS/MS
Bibliographic record
Abstract
The detection of hormone abuse for growth promotion in food animal production is a global concern. Initial testing for hormones in Canada was directed at the compounds approved for use in beef cattle, melengestrol acetate, trenbolone acetate and zeranol, and the banned compound diethylstilbestrol (DES). No hormonal growth promoters are approved for use in veal production in Canada. However, instances of use of trenbolone and clenbuterol were detected in Canada in the 1990s. During the development of a new analytical method for testosterone and progesterone, there were reports of suspicious injection sites being found in veal calves. Upon implementation of the method, analysis of investigative samples revealed significant residues of testosterone in some injection sites. To prove that the source of these residues was exogenous, a fully validated method for hormone esters was developed to confirm the presence of exogenous hormones in these injection sites. The QUECHERS model was employed in methods development and resulted in a simple, effective extraction technique that consisted of sample pre-homogenization, liquid/liquid partitioning, extract dilution, filtration and use of LC/MS/MS to provide detection selectivity. The result was an adaptable MS/MS confirmation technique that meets the needs of Canadian regulatory authorities to confirm the misuse of injectable testosterone, and potentially other hormones, in food animal production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".