Pharmacokinetic modelling of a once-daily dosing regimen for intravenous tobramycin in paediatric cystic fibrosis patients
Bibliographic record
Abstract
OBJECTIVES: This study was designed to determine an optimal dose range for the once-daily dosing (ODD) of tobramycin in the treatment of an acute pulmonary exacerbation in paediatric cystic fibrosis (CF) patients. In addition, we aimed to assess whether certain patient characteristics affect tobramycin pharmacokinetics and, therefore, dosing. METHODS: Patient characteristics and pharmacokinetic parameters of patients receiving tobramycin three times daily from 1 January 1992 to 31 October 2005 were analysed using univariate analysis and multiple linear regression to determine statistically significant relationships and to derive dosing models. The binary partitioning method was used to derive critical values to determine stratification within the chosen dosing model. RESULTS: Using multiple linear regression, age and sex were significantly associated with the volume of distribution divided by the body weight (V/kg). By the binary partitioning method, the critical value for age was 13.75 years. CONCLUSIONS: Age and sex were used to derive an ODD regimen for tobramycin in paediatric CF. Using a target peak concentration range of 25-35 mg/L, the initial dose for female CF patients at least 14 years of age was calculated to be 7 mg/kg/day given intravenously as a single daily dose. All other CF patients would receive an initial dose of 9 mg/kg/day given intravenously as a single daily dose. These dosing guidelines will require prospective evaluation for safety and efficacy.
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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 | 0.003 |
| 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 | 0.001 | 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".