Intravenous Ampicillin/Sulbactam in Critically Ill Dogs has Variable Pharmacokinetics
Bibliographic record
Abstract
Achieving therapeutic plasma concentrations is essential for effective antimicrobial drug (AMD) treatment. Critical illness alters drug distribution and clearance, potentially impacting AMD effectiveness. We conducted a prospective observational study in 25 critically ill dogs to evaluate the pharmacokinetics (PK) of intravenous (IV) ampicillin/sulbactam and achievement of the efficacy target of ≥ 50% of the dosing interval with unbound plasma drug concentrations above the minimum inhibitory concentration (fT > MIC). All dogs received IV ampicillin/sulbactam from a commercial formulation at a dosage of 20 mg/kg ampicillin/10 mg/kg sulbactam. Plasma concentrations were measured using liquid chromatography-mass spectrometry. PK modeling determined best-fit compartmental models, and Monte Carlo simulations evaluated the probability of target attainment for bacterial MICs. A one-compartment model best described ampicillin PK, while a two-compartment model fit sulbactam. Monte Carlo simulations indicated a 90% probability that ampicillin at 20 mg/kg IV q8 h would achieve the Clinical and Laboratory Standards Institute (CLSI) veterinary breakpoint of 0.25 μg/mL for > 50% of the dosing interval. There was only a 10% probability of achieving the human breakpoint of 8 μg/mL. At 0.25 μg/mL, most Enterobacterales isolates would be resistant. The ampicillin/sulbactam dosage tested meets veterinary CLSI standards for ampicillin but might not effectively treat Enterobacterales infections in critically ill dogs.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".