New Treatments for Emerging Cystic Fibrosis Pathogens other than Pseudomonas
Bibliographic record
Abstract
The development of antimicrobial treatments for respiratory pathogens in cystic fibrosis (CF) has been an integral component to the increased survival of CF patients over the past fifty years. Despite significant treatment advances, however, respiratory failure secondary to chronic bacterial pulmonary infection remains the primary cause of death in CF patients. The purpose of this review is to discuss emerging pathogens (other than Pseudomonas) in CF by describing the characteristics of the organism, their clinical significance in CF, their mechanisms of antimicrobial resistance and the current treatment approaches including newer pharmaceutical modalities. This review will focus on the following pathogens: Burkholderia cepacia complex, Stenotrophomonas maltophilia, Achromobacter xylosoxidans, methicillin-resistant Staphylococcus aureus and nontuberculous mycobacteria The goal is to familiarize the reader with the challenges in treating pulmonary infections in CF caused by multi-drug resistant pathogens and to highlight some of the newer pharmaceutical treatments that are currently the focus of intense research. Keywords: Cystic fibrosis, Burkholderia cepacia, Stenotrophomonas maltophilia, Achromobacter, respiratory pathogens, nontuberculous mycobacteria, multi-drug resistant pathogens, β-lactam drugs, chronic infection, biofilm formation
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".