Beta-lactam antibiotic resistance among Escherichia coli isolates from patients with urinary tract infections in Vietnam: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To investigate the evolving patterns of antimicrobial resistance against beta-lactam antibiotics in Escherichia coli isolates from urinary tract infection patients in Vietnam, covering the period from 2005 to 2022. Methods: 29 Descriptive studies published between 2010 and 2022 in English and Vietnamese were included in the analysis. Data on resistance rates to beta-lactam antibiotics, including cephalosporins and carbapenems, were extracted and analyzed. Weighted pooled resistance rates were calculated using random-effects models. Annual trends in resistance were assessed using linear regression analysis. Results: Among the beta-lactam antibiotics studied, Escherichia coli exhibited varying levels of resistance, with cephalosporins showing higher resistance rates compared to carbapenems. Weighted pooled resistance rates were 66% for cefotaxime, 65% for ceftriaxone, 54% for ceftazidime, and 56% for cefepime. In contrast, carbapenems demonstrated lower resistance rates, with weighted pooled resistance rates ranging from 3% to 4% for meropenem, ertapenem, and imipenem. Resistance rates were also observed for amoxicillin/clavulanate (35%), ticarcillin/clavulanate (26%), and piperacillin/tazobactam (12%). Significant annual increases in resistance were noted for imipenem (0.56%, P <0.001). Conclusions: This study highlights the critical issue of antimicrobial resistance in urinary tract infections in Vietnam and emphasizes the importance of prudent antibiotic use and the regular monitoring of resistance patterns. These insights are useful for guiding healthcare professionals in optimizing treatment strategies and for policymakers in formulating evidence-based clinical guidelines to combat antibiotic resistance effectively.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".