Tobramycin a Priori Dosing Regimens Based on PopPK Model Simulations in Critically Ill Patients: Are They Transferable?
Bibliographic record
Abstract
BACKGROUND: In recent years, multiple population pharmacokinetic models have been developed for drugs such as tobramycin that need therapeutic drug monitoring. Some of these models have been used to develop a priori dosing regimens for their respective populations. However, these dosing regimens may not apply to other populations. Therefore, this study aimed to evaluate tobramycin population pharmacokinetic models in critically ill patients and establish an adequate dosing regimen. METHODS: Evaluated models were identified from a literature review of aminoglycoside population pharmacokinetic models in critically ill patients. After retrospective data collection in 2 Quebec hospitals, external evaluation and model re-estimation were performed with NONMEM (v7.5) to assess imprecision and bias values. Dosing regimens were simulated and compared between the best-performing model and its re-estimated counterparts. RESULTS: None of the 3 evaluated models showed acceptable imprecision or bias values in the data sets of the 19 patients. Similar percentages of target attainment were obtained for the original and re-estimated models after the dosing regimen simulations. CONCLUSION: Although the predictive performance evaluation criteria were inadequate, the original and re-estimated models yielded similar results. This raises the question of what a priori bias and imprecision thresholds should be defined as acceptable for the external evaluation of models to be applied in clinical practice. Studies evaluating the impact of these thresholds are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".