Antibiotic treatment of pediatric infections in primary healthcare setting: evaluation and comparison of 80 national treatment guidelines with the WHO AWaRe book recommendations
Bibliographic record
Abstract
Background: Antibiotic recommendations for pediatric infections in national standard treatment guidelines (STGs) vary widely, particularly for Access and Watch antibiotics. The WHO AWaRe book recommends Access antibiotics as first-line treatment for over 80% of common infections managed in primary healthcare. This study aims to evaluate the agreement between first and second-line antibiotics in national STGs with AWaRe book recommendations and the inclusion of these antibiotics in Essential Medicine Lists (EMLs). Methods: National STGs of 80 countries were systematically collected from databases and grey literature (up to May 2025). Antibiotic recommendations for the ten most common primary healthcare infections in children were compared with the WHO AWaRe book (2022), the WHO Essential Medicines List for children (EMLc) and national Essential Medicines Lists (nEMLs) where available. Findings: A median of eight STGs per country were collected, with higher numbers in LMICs due to guidelines for cholera and enteric fever. A total of 1124 first-line and 841 second-line antibiotic recommendations were identified. Over 70% of first-line recommended treatments were Access antibiotics, while Watch antibiotics accounted for more than 50% of second-line recommended treatments. First-line recommendations showed strong agreement with WHO guidance, whereas second-line treatments exhibited lower agreement and greater variability across regions. More than 80% of first-line antibiotics were included in the EMLc and nEMLs, although some high-income countries lacked nEMLs. Interpretation: First-line antibiotic recommendations in national pediatric STGs largely align with the WHO AWaRe book guidance focusing on Access antibiotic use. In contrast, second-line treatments vary considerably, commonly recommending Watch antibiotics. Strengthening the evidence base of national STGs and aligning second-line recommendations with the WHO AWaRe book could help meet the 79th UNGA High-Level Meeting on AMR target, which aims for 70% of all human antibiotic use to come from the Access group. Funding: PRIN 2022 "A Cluster randomized clinical trial to change Antibiotic Prescribing behavior in Outpatient pediatric primary care setting in Italy (CAPO project)", funded in the framework of the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, funded by the European Union-Next Generation EU, Project 2022A7LA2W, CUP C53D23006050006.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".