Antimicrobial Stewardship in Pediatric Emergency Medicine: A Narrative Exploration of Antibiotic Overprescribing, Stewardship Interventions, and Performance Metrics
Bibliographic record
Abstract
Antibiotic overprescribing is prevalent in pediatric emergency medicine, influenced by clinician-caregiver dynamics and diagnostic uncertainties, and poses substantial risks such as increasing antibacterial resistance, adverse drug reactions, and increased healthcare expenditures. While antimicrobial stewardship programs have proven effective in optimizing antibiotic use within inpatient healthcare settings, their implementation in pediatric emergency medicine presents specific challenges. Existing biomarkers like white blood cell count, C-reactive protein, procalcitonin, and presepsin have limitations in their ability to distinguish (serious) bacterial infections from other etiologies of fever. Furthermore, rapid antigen detection tests and guidelines aimed at guiding antibiotic prescriptions for children have not consistently reduced unnecessary antibiotic use. To improve antibiotic prescribing practices, potential strategies include the utilization of decision support tools, audit and feedback, establishing follow-up procedures, implementing safety netting systems, and delivering comprehensive training and supervision. Notably, host genome signatures have also gained attention for their potential to facilitate rapid and precise diagnoses of inflammatory syndromes. Standardized metrics are crucial for evaluating antimicrobial use within pediatric healthcare settings, enabling the establishment of benchmarks for assessing antibiotic utilization, quality enhancement initiatives, and research endeavors.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".