Multiple Combination Bactericidal Antibiotic Testing for Patients with Cystic Fibrosis Infected with <i>Burkholderia cepacia</i>
Bibliographic record
Abstract
Most Burkholderia cepacia strains are resistant to many, or all, of the antibacterial agents commonly used in cystic fibrosis (CF), and selection of appropriate antibiotics for treatment of pulmonary exacerbations is therefore difficult. We developed a technique for rapid in vitro testing of multiple antibiotic combinations for B. cepacia isolates. For each of 119 multi-drug-resistant isolates of B. cepacia, our multiple combination bactericidal test (MCBT) studied the bactericidal activity of 10 to 15 antimicrobial agents using 225 +/- 97 single, double, and triple antibiotic combinations. Of the 119 isolates, 50% were resistant to all single antibiotics tested, 8% were resistant to all two-drug antibiotic combinations, but all were inhibited by at least one bactericidal triple-drug combination. When used alone, meropenem, ceftazidime and high-dose tobramycin (200 microg/ml) were bactericidal against only 47, 15, and 14% of in vitro isolates, respectively. Using a double antibiotic combination improved bactericidal activity; meropenem-minocycline, meropenem-amikacin, and meropenem-ceftazidime combinations were bactericidal against 76, 73, and 73% of isolates, respectively. However, 47% of isolates demonstrated antagonism (growth of an organism when a second antibiotic was added to a bactericidal single antibiotic). Triple antibiotic combinations that contained tobramycin, meropenem, and an additional antibiotic were most effective, and were bactericidal against 81 to 93% of isolates. We conclude that triple-antibiotic combinations are more likely than double and single antibiotic combinations to be bactericidal against B. cepacia in vitro. MCBT testing is a useful technique to help clinicians decide on appropriate nonantagonistic combination antibiotic therapy for patients with CF infected with B. cepacia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".