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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".