Antimicrobial Stewardship in Pediatric Emergency Medicine: A Narrative Exploration of Antibiotic Overprescribing, Stewardship Interventions, and Performance Metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it