Pharmacokinetics and lung and muscle concentrations of tulathromycin following subcutaneous administration in white‐tailed deer (<i>Odocoileus virginianus</i>)
Bibliographic record
Abstract
Respiratory tract infections are common in farmed North American white-tailed deer (Odocoileus virginianus). Tulathromycin is approved for use in cattle but not deer but is often employed to treat deer. The pharmacokinetic properties and lung and muscle concentrations of tulathromycin in white-tailed deer were investigated. Tulathromycin was administered to 10 deer, and then, serum, lung, and muscle tulathromycin concentrations were measured using liquid chromatography-mass spectrometry (LC-MS). The mean maximal serum tulathromycin concentration in deer was 359 ng/mL at 1.3 h postinjection. The mean area under the serum concentration-time curve, apparent volume of distribution, apparent clearance, and half-life was 4883 ng·h/mL, 208 L/kg, 0.5 L/h/kg, and 281 h (11.7 days), respectively. The maximal tulathromycin concentration in lung and muscle homogenate from a single animal was 4657 ng/g (14 days) and 2264 ng/g (7 days), respectively. The minimum concentrations in lung and muscle were 39.4 ng/g (56 days) and 9.1 ng/g (56 days), respectively. Based on similarity in maximal serum concentrations between deer and cattle and high lung concentrations in deer, we suggest the recommended cattle dosage is effective in deer. Tissue concentrations persisted for 56 days, suggesting a need for longer withdrawal times in deer than cattle. Further tissue distribution and depletion studies are necessary to understand tulathromycin persistence in deer tissue; clinical efficacy studies are needed to confirm the appropriate dosage regimen in deer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".