Systematic Review of Antibiotic Resistance Rates Among Gram-Negative Bacteria in Children With Sepsis in Resource-Limited Countries
Bibliographic record
Abstract
BACKGROUND: Gram-negative antimicrobial resistance (AMR) is of global concern, yet there are few reports from low- and low-middle-income countries, where antimicrobial choices are often limited. METHODS: This study offers a systematic review of PubMed, Embase, and World Health Organization (WHO) regional databases of Gram-negative bacteremia in children in low- and low-middle-income countries reporting AMR since 2001. RESULTS: Data included 30 studies comprising 71 326 children, of whom 7056 had positive blood cultures, and Gram-negative organisms were isolated in 4710 (66.8%). In neonates, Klebsiella pneumoniae median resistance to ampicillin was 94% and cephalosporins 84% in Asia; 100% and 50% in Africa. Large regional variations in resistance rates to commonly prescribed antibiotics for Salmonella spp. were identified. Multidrug resistance (resistance to ampicillin, chloramphenicol, and cotrimoxazole) was present in 30% (interquartile range [IQR], 0-59.6) in Asia and 75% (IQR, 30-85.4) in Africa. CONCLUSIONS: There is a need for an international pediatric antimicrobial resistance surveillance system that collects local epidemiological data to improve the evidence base for the WHO guidance for childhood Gram-negative bacteremia.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".