Does Prolonged Infusion Allow Lower Daily Dose of Meropenem than Bolus Dosing?
Bibliographic record
Abstract
and dosing schedules have been selected on the basis of these characteristics. However, application of the known pharmacodynamics of meropenem could perhaps be used to alter the timing of delivery, in turn allowing the use of lower doses of meropenem with equivalent efficacy and net cost savings. To date, studies assessing the potential benefit of prolonged intermittent infusions of meropenem have used Monte Carlo simulations. 6,7 A Monte Carlo simulation is a statistical tool that attempts to mimic real-world situations by generating hypothetical trial data based on variation within the population for a num ber of characteristics. In the case of meropenem, a Monte Carlo simulation would use population data on clearance, volume of distribution, and MIC to generate thousands of hypothetical patients. Such a simulation could be used to measure the consistency of various dosage regimens in achieving surrogate markers of efficacy. To quantify consistency within the theoretical population, the portion of the population achieving the surrogate marker is calculated; this is termed the cumulative fraction of response. 6,7
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".