Efficacy and safety of ceftazidime-avibactam versus standard antibiotic therapy for resistant Gram-negative bacterial infections: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: infections pose a significant clinical challenge due to limited treatment options. Ceftazidime-avibactam (CAZ-AVI) is a novel β-lactam/β-lactamase inhibitor combination developed to address this need. The aim of this study is to assess the clinical effectiveness and safety of CAZ-AVI for these infections. Methods: infection using ceftazidime and avibactam was conducted across both domestic and international databases. The search time frame for each database was set up to February 2024. Following the initial screening process, the quality of the research literature was assessed using the Newcastle-Ottawa Scale (NOS). Subsequently, the odds ratio (OR) value was calculated utilizing either fixed or random effects models, and forest plots were generated. Additionally, a sensitivity analysis was performed by excluding studies with the highest weight, and the bias within the literature was evaluated through the construction of a funnel plot. Results: A total of 2,203 publications were identified, and 8 studies were deemed eligible for analysis. The findings from the meta-analysis revealed that in comparison to the standard antibiotic treatment group, the CAZ-AVI group exhibited superior clinical efficacy and a higher rate of bacterial clearance. There was a decrease in adverse reactions and mortality in the CAZ-AVI group. Nevertheless, no statistically significant variances were observed in procalcitonin (PCT) and C-reactive protein (CRP) levels between the two groups. Sensitivity analysis indicated the stability and reliability of the combined effect size results. Conclusions: infections, making them a more favorable choice for clinical use.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".