Approach to resistant gram-negative bacterial pulmonary infections in patients with cystic fibrosis
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Patients with cystic fibrosis are living longer with chronic pulmonary bacterial infections. One consequence of antibiotic treatment of these chronic infections has been the increasing prevalence of antibiotic resistance seen in bacterial isolates recovered from patients with cystic fibrosis. RECENT FINDINGS: Bacteria such as Pseudomonas aeruginosa and Burkholderia cepacia are able to acquire antibiotic resistance by either spontaneous mutation or gene transfer via plasmids or integrins. In addition, bacteria survive by forming antibiotic-resistant biofilms within the airways of patients with cystic fibrosis. Therapeutic approaches to dealing with antibiotic-resistant bacterial pulmonary infections include the use of in vitro synergy testing to determine optimal double antibiotic combinations or multiple-combination bactericidal testing to determine bactericidal double and triple antibiotic combinations to use against the bacteria in the clinical setting of acute exacerbations. SUMMARY: Therapy for antibiotic-resistant bacterial infections in cystic fibrosis involves the use of new laboratory methods (synergy testing or multiple-combination bactericidal testing) to optimize antibiotic treatment strategies. Clinical trials are required to address whether treatment guided by susceptibility testing improves clinical outcomes. Future novel approaches will likely include drugs that can disrupt bacterial biofilm formation and the use of cationic peptide antimicrobial compounds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".