Impact of Piperacillin Unbound Fraction Variability on Dosing Recommendations in Critically Ill Patients
Bibliographic record
Abstract
A common approach to assess the efficacy of piperacillin is to firstly measure the total concentration, and to afterwards apply a theoretical unbound fraction of 70% to obtain the unbound concentration. However, hypoalbuminemia is a common phenomenon in critically ill patients, resulting in variations in unbound fraction. Therefore, we aimed to simulate the impact of piperacillin unbound fraction fluctuations on the predictive performance of a population pharmacokinetic model and on dosing recommendations of piperacillin. Unbound factors of 70, 75, 80 and 85% were applied to total concentrations of piperacillin administered by continuous infusion from an external dataset. A validated model was used for assessment of predictive performance and to estimate patient clearance. Dosing simulations were performed to evaluate target attainment. Variation in unbound fraction caused minimal impact on piperacillin clearance and target attainment but revealed to influence model evaluation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".