Evaluation of Dilution Susceptibility Testing Methods for Aztreonam in Combination with Avibactam against Enterobacterales
Bibliographic record
Abstract
Increasing antibiotic resistance and emergence of pan-resistant isolates threaten the ability to control infections and to provide many other medical interventions such as surgery and chemotherapy, among others. New therapies are required to control emerging resistance mechanisms, including the increase in metallo-β-lactamases. Some new antibiotic combinations provide coverage against highly resistant isolates but are unable to target organisms that produce metallo-β-lactamases. Aztreonam in combination with avibactam provides a broad spectrum of activity against highly resistant isolates that also targets metallo-β-lactamase-producing organisms. An important part of drug development is the ability for clinical labs to determine the susceptibility of isolates to the antimicrobial. This manuscript investigates the in vitro susceptibility testing of aztreonam/avibactam with nonstandard testing conditions and a correlation study between broth microdilution and agar dilution against clinical isolates encoding a variety of resistance mechanisms. Overall, aztreonam/avibactam was generally unaffected by changes in testing conditions and showed strong agar/broth correlation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".