Ceftazidime-avibactam activity against Gram-negative respiratory isolates collected between 2018 and 2019
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess the in vitro activity of ceftazidime-avibactam (CAZ-AVI) and a panel of comparator agents against isolates of Enterobacter spp., Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa collected in 2018 and 2019 by different centres worldwide from patients with respiratory tract infections. METHODS: Susceptibility and minimum inhibitory concentration (MIC) of all organisms were determined using broth microdilution methodology for CAZ-AVI, and a panel of comparator antimicrobial agents by a central reference laboratory according to Clinical and Laboratory Standards Institute guidelines and European Committee on Antimicrobial Susceptibility Testing guidelines. RESULTS: 8 µg/ml). CAZ-AVI was also active (susceptibility >85%) against MDR isolates for all organisms except P. aeruginosa. Only a small proportion (<10%) of all isolates of Enterobacter spp. and E. coli were identified as XDR compared to isolates of K. pneumoniae and P. aeruginosa isolates (>20%). Susceptibility to CAZ-AVI was high (>70%) among XDR isolates of Enterobacter spp., K. pneumoniae, and E. coli, compared to P. aeruginosa (<70%). Among the comparator agents, only colistin showed consistent activity across all the organisms (>85%). CONCLUSION: Gram-negative respiratory isolates collected in 2018-2019 showed high susceptibility to CAZ-AVI; CAZ-AVI represents a potential treatment option against infection caused by these organisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".