Determination of Tobramycin Pharmacokinetics in Burn Patients to Evaluate the Potential Utility of Once-Daily Dosing in this Population
Bibliographic record
Abstract
The objective was to determine the pharmacokinetics of tobramycin in critically ill adult burn patients and evaluate a variety of milligram per kilogram (mg/kg) total body weight (TBW) regimens to determine whether practical initial once-daily administration recommendations to attain desired plasma levels could be identified. A retrospective study was conducted in 58 eligible patients who received tobramycin and had at least one set of steady-state levels from which pharmacokinetic parameters could be determined using standard first-order pharmacokinetic equations. Classification and Regression Tree analysis was used to identify whether tobramycin clearance changed with time postburn. Monte Carlo Simulation was used to evaluate initial mg/kg TBW dosing regimens to determine whether a clinically useful once-daily tobramycin recommendation could be made. Tobramycin clearance was significantly greater for patients ≤45 days postburn vs patients >45 days postburn. Once-daily tobramycin dosing for patients ≤45 days postburn of 10 to 13 mg/kg TBW and for patients >45 days postburn of 8 to 10 mg/kg TBW provided levels similar to those known to be effective in nonburn injury patients. Once-daily tobramycin dosing recommendations for burn patients were determined. Variability in pharmacokinetics in this population and change in pharmacokinetics with time postburn injury necessitate monitoring of tobramycin levels to ensure targets are met and maintained.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".