Variation in Target Attainment of Beta‐Lactam Antibiotic Dosing Between International Pediatric Formularies
Bibliographic record
Abstract
As antimicrobial susceptibility of common bacterial pathogens decreases, ensuring optimal dosing may preserve the use of older antibiotics in order to limit the spread of resistance to newer agents. Beta-lactams represent the most widely prescribed antibiotic class, yet most were licensed prior to legislation changes mandating their study in children. As a result, significant heterogeneity persists in the pediatric doses used globally, along with quality of evidence used to inform dosing. This review summarizes dosing recommendations from the major pediatric reference sources and tries to answer the questions: Does beta-lactam dose heterogeneity matter? Does it impact pharmacodynamic target attainment? For three important severe clinical infections-pneumonia, sepsis, and meningitis-pharmacokinetic models were identified for common for beta-lactam antibiotics. Real-world demographics were derived from three multicenter point prevalence surveys. Simulation results were compared with minimum inhibitory concentration distributions to inform appropriateness of recommended doses in targeted and empiric treatment. While cephalosporin dosing regimens are largely adequate for target attainment, they also pose the most risk of neurotoxicity. Our review highlights aminopenicillin, piperacillin, and meropenem doses as potentially requiring review/optimization in order to preserve the use of these agents in future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".