A single laboratory‐validated LC‐MS method for the analysis of tulathromycin residues in bison and deer sera and selected tissues of white‐tailed deer
Bibliographic record
Abstract
The performance characteristics of a newly developed liquid chromatography-mass spectrometry (LC-MS) method were validated and demonstrated to be fit for purpose in a pharmacokinetic and tissue depletion study of white-tailed deer and bison. Tulathromycin was extracted from bison and deer sera with acetonitrile or trifluoroacetic acid and K2 HPO4 (pH 6.8) buffer solution and cleaned up on a conditioned Bond-Elut cartridge. Tulathromycin, retained on the cartridge; it was eluted with methanol containing 2% formic acid, dried, re-constituted in methanol/1% formic acid, and analyzed by LC-MS. The limit of quantification (LOQ) of the method was 0.6 ng/mL in serum and 0.6 ng/g in tissue with RSDs ≤ 10% and accurate over the linear calibration range of 0.8-100 ng/mL for bison serum, 0.6-50 ng/mL for deer serum, 100-2500 ng/g for deer muscle tissue, and 500-5000 ng/g for deer lung tissue, all with coefficients of determination, r(2) ≥0.99. The validated method was used to quantify the concentration of tulathromycin residues in serum of bison and deer and selected tissue (lung and muscle tissue) samples obtained from 10 healthy, white-tailed deer that were administered the therapeutic dose approved for cattle (i.e., a single 2.5 mg/kg subcutaneous injection of tulathromycin in the neck). The deer were included in a tulathromycin drug depletion study. © 2016 Her Majesty the Queen in Right of Canada. Drug Testing and Analysis © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".