Ampicillin pharmacokinetics in azotemic and healthy dogs
Bibliographic record
Abstract
BACKGROUND: Little is known about effects of factors such as kidney disease, affecting ampicillin pharmacokinetics in dogs. OBJECTIVES: Determine the pharmacokinetics of ampicillin after a single intravenous dose in healthy and azotemic dogs. ANIMALS: Nine dogs presenting with acute kidney injury and 10 healthy dogs. METHODS: This was a prospective study. An ampicillin dose of 22.2 mg/kg (mean dose) was administered once intravenously. Blood samples were obtained at timed intervals (just before administration, 1, 2, 4, 12, and 24 hours), analyzed using high-pressure liquid chromatography followed by pharmacokinetic analysis of the plasma drug concentrations. RESULTS: Peak ampicillin concentration (mcg/mL; 97.07 (36.1) vs 21.3 (50.26)), P<.001 (geometric mean (coefficient of variation, CV%)), half-life (hours; 5.86 (56.55) vs 0.97 (115.3)), P<.001) and AUC (h × mcg/mL; 731.04 (83.75) vs 33.57 (53.68)), P<.001) were greater in azotemic dogs than in healthy dogs. Azotemic dogs also had significantly lower clearance (30.06 (84.19) vs 655.03 (53.67); mL/kg h, P < .001) and volume of distribution (253.95 (30.14) vs 916.93 (135.24); mL/kg, P <.001) compared to healthy dogs. CONCLUSION AND CLINICAL IMPORTANCE: Increased drug concentrations and slower clearance of ampicillin in azotemic dogs could have clinical importance in contributing to antibiotic associated morbidity requiring indicating the need to adjust ampicillin dosing in dogs with decreased kidney function.
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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) | 0.000 | 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".