A Meta-analysis of Antipseudomonal Penicillins and Cephalosporins in Pediatric Patients With Fever and Neutropenia
Bibliographic record
Abstract
BACKGROUND: Antipseudomonal penicillins (APP) and antipseudomonal cephalosporins (APC) play important roles in the management of pediatric patients with fever and neutropenia (FN). Our primary objective was to describe the risk of treatment failure in children treated with an APP or APC as initial empiric therapy for FN. Our secondary objectives were to compare APP with APC and third- with fourth-generation APC as initial empiric therapy in this population. METHODS: We performed electronic searches of Ovid Medline, EMBASE, and the Cochrane Central Register of Controlled Trials, limiting studies to prospective pediatric trials in FN in which at least 1 treatment arm consisted of an APP or APC antibiotic with or without an aminoglycoside. Data abstraction was conducted by 2 independent reviewers. RESULTS: From 7281 reviewed articles, 41 studies comprising 51 treatment regimens were included in the meta-analysis. Treatment failure, including antibiotic modification, occurred in 34% and 41% of patients treated with APP and APC monotherapy, respectively, and 41% and 33% of patients treated with APP- and APC-aminoglycoside combination therapy, respectively. There were no statistically significant differences in treatment failure including modification, mortality, or adverse events when comparing APP with APC monotherapy, APP with APC combination therapy, or third- with fourth-generation APC therapy. CONCLUSIONS: Our meta-analysis suggests that APP and APC monotherapy, as well as combination therapy with an aminoglycoside, are efficacious and safe therapeutic options for the empiric management of pediatric patients with FN. Specific antibiotic selection should be based on other important factors, such as cost, availability, and local epidemiologic and resistance patterns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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".