Multiple Combination Bactericidal Antibiotic Testing for Patients with Cystic Fibrosis Infected with Multiresistant Strains of <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
We developed a rapid in vitro antibiotic susceptibility test to screen double- and triple-antibiotic combinations for bactericidal activity against 75 multiresistant Pseudomonas aeruginosa isolates referred from 44 cystic fibrosis (CF) patients. When used alone, the most effective intravenous antibiotic, meropenem, was bactericidal against only 44% of the isolates. High-dose tobramycin (200 microg/ml; concentrations achievable by aerosol administration) was bactericidal against 72% of isolates. Adding a second antibiotic significantly improved bactericidal activity. The most effective double-antibiotic combinations contained high-dose tobramycin plus meropenem, piperacillin/tazobactam, or ciprofloxacin, and were bactericidal against 88 to 94% of the isolates. Excluding high-dose tobramycin, the most effective intravenous double-antibiotic combinations contained meropenem plus ciprofloxacin, tobramycin (4 microg/ml), or cefipime, and were bactericidal against 85%, 71%, and 70% of isolates, respectively. Adding a third antibiotic did not significantly improve inhibition in vitro. We conclude that double-antibiotic combinations containing meropenem or high-dose tobramycin show the best bactericidal activity in vitro against multiresistant strains of P. aeruginosa. Addition of a third antibiotic to these double-antibiotic combinations may be unnecessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".