Efficacy and safety of ceftazidime–avibactam versus polymyxins in the treatment of carbapenem-resistant Enterobacteriaceae infection: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Carbapenem-resistant Enterobacteriaceae is increasingly recognised as a significant public health concern. Ceftazidime-avibactam (CAZ-AVI) and polymyxins are considered as the last therapeutic options worldwide. This is the first meta-analysis of recently published data to compare the clinical efficacy and safety of CAZ-AVI with polymyxins in the treatment of carbapenem-resistant Enterobacteriaceae infections. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Embase and the Cochrane Library were systematically searched, for publications in any language, from database inception to February 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies comparing the clinical efficacy and safety of CAZ-AVI with polymyxins were included. Mortality, clinical success, microbiological eradication and nephrotoxicity were assessed as the main outcomes. DATA EXTRACTION AND SYNTHESIS: Literature screening, data extraction and the quality evaluation of studies were conducted by two researchers independently, with disagreements resolved by another researcher. The Newcastle-Ottawa Scale was used to assess the bias risk for the included studies. Review Manager V.5.3 was employed for the meta-analysis. RESULTS: =0, p>0.05). CONCLUSION: Available evidence suggested that CAZ-AVI treatment held a dominant position with respect to efficacy and safety compared with polymyxins in carbapenem-resistant Enterobacteriaceae infections. However, the analysis included only observational studies, and high-quality, large-scale, multicentre, double-blind randomised controlled trials are needed to confirm the advantage of CAZ-AVI.
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.
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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.057 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".